{"as_of":"2026-08-16T06:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2298365e9a6f02fc922da356e3b8db21d2bf90d9df30a322ac8bc51448ccbd07","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:03:56.351701Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:03:54.577559Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-10T21:26:48.694682Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12217","snapshot_observed_at":"2026-08-11T15:03:54.577559Z","title":"Comprehensive survey on adversarial examples in cyberse- curity: Impacts, challenges, and mitigation strategies,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.12218","last_updated":"2025-02-23T21:26:57Z","snapshot_observed_at":"2026-08-15T02:46:55.467233Z","submitted_at":"2024-12-16T01:57:53Z","title":"Accelerating Sparse Graph Neural Networks with Tensor Core Optimization","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:54.577559Z"},"links":{"cited_paper":"/paper/2412.12217","citing_paper":"/paper/2412.12218"},"observation_digest":"sha256:d6847cbc37d3bfd031d03e9b3a1215d98f4af39ecc064d29a4e809e874d6eb60","observation_id":"b8c2dd55-77a1-44ca-818e-5058e491f866","resolution":{"observed_at":"2026-08-11T15:03:54.577559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"cited_work":{"arxiv_id":"2412.12217","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.12217","snapshot_observed_at":"2026-08-10T21:26:48.694682Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","venue":"cs.CR","work_id":"e94e9b7f-b2c7-4395-8a44-89a66adf8e5c","year":2024},"citing_paper":{"arxiv_id":"2501.04841","last_updated":"2025-04-19T15:20:24Z","snapshot_observed_at":"2026-08-15T20:35:40.743467Z","submitted_at":"2025-01-08T21:07:25Z","title":"Blockchain-Based Secure Vehicle Auction System with Smart Contracts","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:26:47.870487Z"},"links":{"cited_paper":"/paper/2412.12217","citing_paper":"/paper/2501.04841"},"observation_digest":"sha256:5f93e26b7cbc80b76f68637acd37ef018f201024f739a053099c10508faa79a4","observation_id":"b49e0beb-8cb6-431e-8a35-970462f12ade","resolution":{"observed_at":"2026-08-10T21:26:48.700113Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.12217/citation-record","integrity":"/paper/2412.12217/integrity","json":"/paper/2412.12217/citation-record.json","paper":"/paper/2412.12217"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.392057Z","title":"Botnet detection using r ecur- rent variational autoencoder,","venue":null,"work_id":"463b1d17-42cc-4374-8279-67574d3ef11a","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.372474Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:fc8f893127ba7a8b757b6cc9240e78ceff85d5cd9593b51ce6a28d92bf582281","observation_id":"c8fee6cf-20db-44ee-b68d-2018974ddf9a","resolution":{"observed_at":"2026-08-11T15:03:58.402026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.360385Z","title":"A visualized botnet detection system based deep learning for the internet of things networks of smart cities ,","venue":null,"work_id":"73cc8280-686b-4531-99b8-24fb4aac6e7b","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.381433Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:15e280d790075d07e7c929eb3d2ba062de43eb72728110d735ef41537f40fd9e","observation_id":"973d196e-4d26-4c9e-b21f-340f6c3d4a64","resolution":{"observed_at":"2026-08-11T15:03:58.375295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.328927Z","title":"Detecting DGA domains with recurrent neural net works and side information,","venue":null,"work_id":"43c6fb7a-e581-4db8-a835-b8beabaa1524","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.392893Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:a67d802691633c373ff3a772894c4e7f3947abaff644f67f494de1db01fbdb7b","observation_id":"8ac221db-7f22-435e-ae7b-fd7442bcd769","resolution":{"observed_at":"2026-08-11T15:03:58.339253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.294669Z","title":"A LST M- based framework for handling multiclass imbalance in DGA bo tnet detection,","venue":null,"work_id":"db5a3030-6507-416f-b845-10cc169be3af","year":2018},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.403343Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:1671c59e554f536e6420e53aa435f45cfb8f30ce67f2dfa4aec8fb2661f0e6f3","observation_id":"81d85dac-8612-4aa8-b25a-6ed49432f1c0","resolution":{"observed_at":"2026-08-11T15:03:58.305540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.271028Z","title":"Detecting st ealthy domain generation algorithms using heterogeneous deep neu ral network framework,","venue":null,"work_id":"acd28ad7-a3c2-4620-bb23-24b550336c70","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.412551Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:bb0220fabf5bc46e72d570e4c006c3c576388b44325757f018e8b7c0602d2b14","observation_id":"5da54e93-8340-4221-9576-206ec63de417","resolution":{"observed_at":"2026-08-11T15:03:58.278554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.233700Z","title":"FGMD: A robust detector aga inst adversarial attacks in the IoT network,","venue":null,"work_id":"b0fb5363-f6e3-4648-8317-967282f87d1e","year":2022},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.423868Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:2b56931206911330bbb63b55f75ec22ba013251fc6593151b3ac25dda92edf33","observation_id":"2d7e19c1-8fc5-4224-bdfe-f3da29e40831","resolution":{"observed_at":"2026-08-11T15:03:58.244518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.203767Z","title":"Adve rsarial attacks against network intrusion detection in IoT systems ,","venue":null,"work_id":"0a9c5d65-9e97-44ea-8846-edeb9d3fa80c","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.432236Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:3ae24529649bca9e05742e964537a144e75384be8620d38e76b057af8ffa0412","observation_id":"bd3223aa-f700-4b82-8171-f2ea45958e0e","resolution":{"observed_at":"2026-08-11T15:03:58.210982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.179188Z","title":"Gene rative adversarial attacks against intrusion detection systems u sing active learning,","venue":null,"work_id":"ce65d047-8204-4505-9378-0ea1cefbcc37","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.440678Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:228b52b5c33811d083d3565c3351a006133d57a42c41079dffec34e59670cfed","observation_id":"c84b781b-740d-4239-913c-77229e8e375b","resolution":{"observed_at":"2026-08-11T15:03:58.185131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.154128Z","title":"MAND A: On Adversarial Example Detection for Network Intrusion Det ection System,","venue":null,"work_id":"40163ebe-f08b-4e03-b252-a040c7b89a02","year":2023},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.448718Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:d10e48b1f981ad0caa060b9d1885a46158a558b79d8d7fb3b9b8d1289f134fcf","observation_id":"f3f32801-4e0b-4db0-8dc7-8cc6f1ef3143","resolution":{"observed_at":"2026-08-11T15:03:58.162597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.130225Z","title":"Biometric face presentation attack detection wit h multi-channel convolutional neural network,","venue":null,"work_id":"025353b1-94de-48bf-b8df-f7cfdede181b","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.459123Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:8531dc8757e5d330b03ddad4c203341726501830cb9e4357fbad10a4b68cdee4","observation_id":"240b8f66-c336-4cf6-a0eb-6b3b9ed492c2","resolution":{"observed_at":"2026-08-11T15:03:58.137524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.097569Z","title":"Deep representations for iris, face, and finger- print spoofing detection,","venue":null,"work_id":"4dfdf8ce-8d4a-42f8-9bc9-da045d1cb2c1","year":2015},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.474311Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:93e75997eb01a0c28b784c241444b1bacbf893ddc0508346e395c30db55bf8ff","observation_id":"a7f8467f-d7b7-4f76-95e0-655b23af9bd4","resolution":{"observed_at":"2026-08-11T15:03:58.104826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.068912Z","title":"Presentatio n attack detection using a tiny fully convolutional network,","venue":null,"work_id":"2206a406-0c5b-4a06-8657-28160e0b51e6","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.489494Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:d087ff35ae5bd2d30f6f30156599a3b6286fe4768b1ad2c19367a5837d7a7481","observation_id":"99697108-f06f-4439-ad07-cb9b7fa28bf3","resolution":{"observed_at":"2026-08-11T15:03:58.080605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:58.030711Z","title":"Deep Boltzmann machines for robust fingerprint spoofi ng attack detection,","venue":null,"work_id":"b1c98c7a-21fd-4f4f-80bd-75890d952c5d","year":2017},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.515385Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:3497b2e0e56e97839f64f7b74241f59a24969019d7e4c4bef6c2bcc0d60853ca","observation_id":"8f8c0007-02c1-4852-8a20-eb5c0e0a149b","resolution":{"observed_at":"2026-08-11T15:03:58.036789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.995842Z","title":"Mob ile encrypted traffic classification using deep learning,","venue":null,"work_id":"d8b54eae-168d-4817-95e2-16dcb324501c","year":2018},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.528762Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:e4daec895c95082fc856e7ad6a6d8ec7f5862594d313b46a14929d221bf1f892","observation_id":"62080924-5aaf-41a1-9b34-6056d9b755e8","resolution":{"observed_at":"2026-08-11T15:03:58.006653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23422","last_updated":"2024-12-16T02:05:19Z","snapshot_observed_at":"2026-08-12T22:11:24.693482Z","submitted_at":"2024-10-30T19:58:46Z","title":"Mitigating Challenges in Ethereum's Proof-of-Stake Consensus: Evaluating the Impact of EigenLayer and Lido","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.23422","snapshot_observed_at":"2026-08-11T15:03:55.538455Z","title":"Mitigating Challenges in Ethereum’s Proof-of- Stake Consensus: Evaluating the Impact of EigenLayer and Lido,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.538455Z"},"links":{"cited_paper":"/paper/2410.23422","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:c87ca3e42dacc0645fa916da8bc43bce6449a78b0c99867ce40d7dc53450d90c","observation_id":"973cb4d3-e47f-4a6f-87ed-4f2dcd687ce7","resolution":{"observed_at":"2026-08-11T15:03:55.538455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.968463Z","title":"Multitask learning for network tr affic classifica- tion,","venue":null,"work_id":"ab30d58c-9e0e-4ff5-af69-00bf071fb701","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.554228Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:557179bfcd499d02671ed471cda217ab139431be3bd225b78304accbece61662","observation_id":"4acacb3b-dcf6-4e96-95f6-6a96eb31f382","resolution":{"observed_at":"2026-08-11T15:03:57.975655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.934173Z","title":"Flowpic: Encrypted interne t traffic classi- fication is as easy as image recognition,","venue":null,"work_id":"bb628fe0-e965-4c9f-bc7c-789f36faf432","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.565110Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:65d97a535d441624bae76f326437bb9b7859320067f61523959d9ce3e68aa5cc","observation_id":"c14d0e38-134a-419f-902e-2fd172a9db4d","resolution":{"observed_at":"2026-08-11T15:03:57.947885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.906564Z","title":"Deep fing erprinting: Undermining website fingerprinting defenses with deep lear ning,","venue":null,"work_id":"a6df0559-bb3f-4e24-928d-609dac7592aa","year":2018},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.579048Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:e821e213c58f01909acf965b9d5e7b97b849e60f0dee6614e651557a08b2fc47","observation_id":"ebbe9d84-6de9-4e71-a969-940fd5a9f752","resolution":{"observed_at":"2026-08-11T15:03:57.919823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.871083Z","title":"Adversarial examples: A survey and experimental eva luation of practical attacks on machine learning for windows malware d etection,","venue":null,"work_id":"26fd086c-988c-4703-9732-66281c4fdca8","year":2021},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.589665Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:3c67ee6affe0e852479cd48132abdd2f579a3f70bc3e80304f14e57bc48e8c16","observation_id":"74c09284-63a0-4fde-99f8-61739cdb37ef","resolution":{"observed_at":"2026-08-11T15:03:57.884164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.841355Z","title":"DL-FHMC: Deep learning-b ased fine-grained hierarchical learning approach for robust mal ware classifi- cation,","venue":null,"work_id":"0acf8c2e-e49b-4a4a-8bc8-25e97b472c85","year":2021},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.607079Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:0a4c269d98d2aad393409e04c66e3150409b5b251afab4e0b8fd23e63b43aa74","observation_id":"78c3a212-3f25-4e17-b579-49562d8f3135","resolution":{"observed_at":"2026-08-11T15:03:57.850793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.766948Z","title":"Soteria: Detecting adversarial ex amples in control flow graph-based malware classifiers,","venue":null,"work_id":"a2006a4b-523d-4d7d-94d1-409b531e1c94","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.633278Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:3726448351f29cd646b514747777aa9fcc4e664b637ff6b06d065030b6aaac50","observation_id":"2c97d575-1f33-4bf6-9874-a037de334e1a","resolution":{"observed_at":"2026-08-11T15:03:57.792922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.712577Z","title":"DeepDGA: A dversarially- tuned domain generation and detection,","venue":null,"work_id":"5e20fb7b-840c-45be-8f2c-895e57269700","year":2016},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.685812Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:ca030bc44d23933cbd3b6f95132ac7f11e7c8f550b4680b6b819cf9ad91956cf","observation_id":"e4fa060c-44c1-4ee5-8cdf-37493cb680e2","resolution":{"observed_at":"2026-08-11T15:03:57.719101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.641113Z","title":"FGMD: A robust detector ag ainst adversarial attacks in the IoT network,","venue":null,"work_id":"474ecf8a-6366-4752-9a78-f8ad87f98b20","year":2022},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.704619Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:7c6a6578988f6a0c05eeb77d944033291a642d5a40d63544ea5d973c70005aab","observation_id":"85ad48b7-80f3-4b15-9448-197a9394b7d7","resolution":{"observed_at":"2026-08-11T15:03:57.662194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.528867Z","title":"Tiki-taka: A ttacking and de- fending deep learning-based intrusion detection systems,","venue":null,"work_id":"0e25eed1-defa-4bbb-a4af-bcfe870deb2e","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.760872Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:c3d420e80634de85e292ea5d8af6ae18228b9f823f01fb6fce78a151ab8f148b","observation_id":"f84320ba-c97a-4b57-b3f4-343f20f2396f","resolution":{"observed_at":"2026-08-11T15:03:57.539459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-08-15T16:41:15.505782Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-11T15:03:55.869778Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.869778Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:40866d3714ad02dd89bfa67aaa8a6af107d526183cc198811995296cf903fadc","observation_id":"9f091c47-48f0-4a30-b98d-ac8d3d924030","resolution":{"observed_at":"2026-08-11T15:03:55.869778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.04435","last_updated":"2016-06-16T08:14:12Z","snapshot_observed_at":"2026-08-14T21:52:47.700386Z","submitted_at":"2016-06-14T16:01:52Z","title":"Adversarial Perturbations Against Deep Neural Networks for Malware Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.04435","snapshot_observed_at":"2026-08-11T15:03:55.906885Z","title":"Adversarial perturbations against deep neural networks f or malware classification,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.906885Z"},"links":{"cited_paper":"/paper/1606.04435","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:8107c6d8cc791fc259bd590c88338f98f5e61a53c373726f0ad069ba4bb86f1b","observation_id":"323236d1-5aeb-440b-9220-d252c233243e","resolution":{"observed_at":"2026-08-11T15:03:55.906885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.290681Z","title":"Adversarial examples for malware detection,","venue":null,"work_id":"d92454f3-1564-4987-80ad-d5e94a8acbef","year":2017},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.914757Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:a9c698a48af542c83f15fa519b863426b708d418e05051b71e8af214e51e40b7","observation_id":"5febab5a-dd20-4b2b-89f7-e2c8b17ee655","resolution":{"observed_at":"2026-08-11T15:03:57.304355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.09735","last_updated":"2019-09-20T22:40:33Z","snapshot_observed_at":"2026-08-12T07:32:25.300999Z","submitted_at":"2019-09-20T22:40:33Z","title":"COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection","version":1},"cited_work":{"arxiv_id":"1909.09735","doi":null,"metadata_source":"pith","pith_arxiv_id":"1909.09735","snapshot_observed_at":"2026-08-11T15:03:56.731840Z","title":"COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection","venue":"cs.CR","work_id":"06a9e367-85db-40bf-ad99-4542eaa7cc04","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.926480Z"},"links":{"cited_paper":"/paper/1909.09735","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:ab5dc9d89f2c1c960396037f6370a7f821757e7ac1ba302b3dca70f44e5ac836","observation_id":"ecc187fc-ef30-4378-9f00-85dbf85cf185","resolution":{"observed_at":"2026-08-11T15:03:56.742515Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.814417Z","title":"Adversarial learning attacks on graph-based Io T malware detection systems,","venue":null,"work_id":"37e86303-a1d7-40a8-9b51-e75402421fcc","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.958252Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:c82b9c1e436fb9700a1affcdbcf8c07d624330bbe0380ee65cd508fd8c0f7ad5","observation_id":"e6c0a39f-2fa0-4e70-80a7-e0f9949ba82d","resolution":{"observed_at":"2026-08-11T15:03:57.823386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.733484Z","title":"Securing malware cogn itive sys- tems against adversarial attacks,","venue":null,"work_id":"02f2fe82-5d4b-4762-810a-17cb7b6c2ae9","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:55.979130Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:0ff83b22e52954e64d9ea7fb1a1ba30229602b42b9256f27a06e532268e26f5d","observation_id":"5e5335ae-0028-46c7-9129-5abf65fd9868","resolution":{"observed_at":"2026-08-11T15:03:57.741340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.268580Z","title":"Dl-fhmc: Deep learning-b ased fine-grained hierarchical learning approach for robust mal ware classifi- cation,","venue":null,"work_id":"224e7e6e-d3e7-4b5a-8f94-fe19f0913c7f","year":2021},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.018472Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:8153f9a8bee76298fe379835b21219adaa08579a6fe54173cd0ca836d6adf13e","observation_id":"b9cb4c5f-83f2-44d8-a88c-6cb841145b93","resolution":{"observed_at":"2026-08-11T15:03:57.274856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.234353Z","title":"DeepDGA: A dversarially- tuned domain generation and detection,","venue":null,"work_id":"63e9c2ae-c0c8-4558-a812-19f5db850c0b","year":2016},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.034603Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:18281d671319ab297b76edf09acdab0b0b9b840f8fc5d09bd213a4277d0a7ed8","observation_id":"638c8133-6904-444f-9d00-4462454d8497","resolution":{"observed_at":"2026-08-11T15:03:57.248511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.680519Z","title":"CharBot: A simple and effective method for evading DGA classifiers,","venue":null,"work_id":"0e8f1a09-ab12-4227-8623-ae846e2c0f8f","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.041209Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:b52d4df3f33478e632e7b9f7386ab86ddbc219d2150398f271a74494b14d1c02","observation_id":"47e597ee-d050-494e-9f2f-80f62df7c16f","resolution":{"observed_at":"2026-08-11T15:03:57.692364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.08909","last_updated":"2019-02-24T09:11:04Z","snapshot_observed_at":"2026-08-15T03:38:22.758510Z","submitted_at":"2019-02-24T09:11:04Z","title":"MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses","version":1},"cited_work":{"arxiv_id":"1902.08909","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.08909","snapshot_observed_at":"2026-08-11T15:03:56.591192Z","title":"MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses","venue":"cs.CR","work_id":"954bfd4c-548f-40c9-8870-8f192d7a0003","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.049059Z"},"links":{"cited_paper":"/paper/1902.08909","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:342742979b5779349b862c7a47ddb2cd95e0d1819dd9d6801bbd458d2da7e593","observation_id":"e7f8abf8-436f-4c2d-96ff-3c1bc7305f1a","resolution":{"observed_at":"2026-08-11T15:03:56.605397Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.183959Z","title":"Khaos: An adversarial neural network DGA with high anti-detection ability,","venue":null,"work_id":"85a3d189-ffee-4d06-87b5-96ff7c03f23c","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.063633Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:3e3718560538cf908573c1cd74cbb293b33ca642c352729b3d7a22f0561a4dfc","observation_id":"92170547-6d1d-4ca0-8e67-65aec28ad2c2","resolution":{"observed_at":"2026-08-11T15:03:57.192798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.147487Z","title":"CLETer: A Charac ter- level Evasion Technique Against Deep Learning DGA Classifie rs,","venue":null,"work_id":"7ad6f009-0b2b-4a95-98df-373a59d4a46f","year":2021},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.076000Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:0a77661746449d6f3cb64856b3b04b93e7af0cf2c70a27a1b4583d871fb103fa","observation_id":"ad2b7661-792e-4918-974b-32e99f93bd0e","resolution":{"observed_at":"2026-08-11T15:03:57.161690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.078396Z","title":"Demystifying the transferability of advers arial attacks in computer networks,","venue":null,"work_id":"cf58c65c-f4ea-4660-b20e-23fcb0df925e","year":2022},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.098591Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:409e709d28881ba822d6f358779a89b48b26b836432eb56cf5e15fb1be3db8e7","observation_id":"f2830372-731c-4504-9faf-a2aa8d7b459a","resolution":{"observed_at":"2026-08-11T15:03:57.106325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.040539Z","title":"Zhang, S","venue":null,"work_id":"d7247a92-b2ee-44cc-9cc9-b588f80bdb15","year":2024},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.107549Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:08af03ee92b77782e1ec06fc82f13b8d08832fbdb31777d6c65aa3385a49b03d","observation_id":"5136c3ef-504d-42d5-a2a3-9fb86542aa17","resolution":{"observed_at":"2026-08-11T15:03:57.060235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.571715Z","title":"Analyzing advers arial attacks against deep learning for intrusion detection in IoT networ ks,","venue":null,"work_id":"1b644a13-61ac-4cbd-b9a3-bc3abace6358","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.127692Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:55556f2788cb84cc38a2304fcade3a94b0ad9bbdf2b7a9c1282992fb102c51d3","observation_id":"b9d14c91-7b64-41aa-876d-ce9fe4c6d95e","resolution":{"observed_at":"2026-08-11T15:03:57.578941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:56.984096Z","title":"Tiki-taka: A ttacking and de- fending deep learning-based intrusion detection systems,","venue":null,"work_id":"2b65796d-e147-41ed-b49a-f859f8300201","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.139030Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:1b32528124688b5302b2796c8e7a7ef4655d54ac43ab423c81fba80570cc3fce","observation_id":"01840edc-6bc7-49fc-85df-f04cb7f07be3","resolution":{"observed_at":"2026-08-11T15:03:56.993770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.597055Z","title":"Gen erative adversarial attacks against intrusion detection systems u sing active learning,","venue":null,"work_id":"8744a875-fe58-457c-bc51-8566aa51d8e5","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.150776Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:780b7d07086639c319bb6f07cafe2a2e7db7641fb2187bf4d1434bcfcb3b4022","observation_id":"1e9def2a-f458-4ab7-92b2-769d98926337","resolution":{"observed_at":"2026-08-11T15:03:57.604143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.619274Z","title":"Adv ersarial attacks against network intrusion detection in IoT systems ,","venue":null,"work_id":"de69977a-622a-4306-8a0f-d494892fb783","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.161918Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:36567b1d11d3434be049a1f57b57a7c1cd1f52524c5f62fcfc88c0b776ce2415","observation_id":"b98b3fb0-cc07-4b2c-9a83-043bc6b5136a","resolution":{"observed_at":"2026-08-11T15:03:57.626556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.418866Z","title":"Adversarial attacks on remote user authentication using b ehavioural mouse dynamics,","venue":null,"work_id":"fe435497-f1c3-44cc-86a8-4572535a08f0","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.178737Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:e7475eb951c63f835e7206bc1b430002bd75e58192a9853c9fdc0838b7afbb65","observation_id":"b3b20723-9281-422a-8db3-4b9053dec651","resolution":{"observed_at":"2026-08-11T15:03:57.431613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.463487Z","title":"V o iceprint mimicry attack towards speaker verification system in smart home,","venue":null,"work_id":"47dac435-7b58-40df-859f-bdd37fbdf0c7","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.191893Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:43f45a299577349048fab8b69747cbdaf4e03c7ecd0bb93df989bb1d0a8e26fb","observation_id":"630e12b6-f525-4f45-abb0-87e8e2c57078","resolution":{"observed_at":"2026-08-11T15:03:57.474553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.490749Z","title":"Attack on practical speaker verification system using u niversal adversarial perturbations,","venue":null,"work_id":"e336b39d-8a62-4c31-99d0-b9938851807d","year":2021},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.197828Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:5866c88239ff591639c5d8797454637b9b37abe7cf6170996b15b75d9571e971","observation_id":"f48ec2c6-b02d-45c1-b39e-242800a79b87","resolution":{"observed_at":"2026-08-11T15:03:57.498253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.03162","last_updated":"2021-05-07T11:00:35Z","snapshot_observed_at":"2026-08-13T19:27:15.419384Z","submitted_at":"2021-05-07T11:00:35Z","title":"Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.03162","snapshot_observed_at":"2026-08-11T15:03:56.208005Z","title":"Adv-makeup: A new imperceptible and transferable att ack on face recognition,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.208005Z"},"links":{"cited_paper":"/paper/2105.03162","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:20dd0b5a0690122cf8aa369553306c3013e2d6642de2515426040035c707098b","observation_id":"5cc65431-f0bd-402a-a9e5-99b3e972c935","resolution":{"observed_at":"2026-08-11T15:03:56.208005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:56.933375Z","title":"Adversarial sample detection for speaker ve rification by neural vocoders,","venue":null,"work_id":"dc5df18b-eaf5-4ebb-b80d-3ce3414bbd4a","year":2022},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.217416Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:3997bd033628778a150e3d0e0f432d5f15b47d48f05fd241922cdccf4336c889","observation_id":"88da0d1c-5a35-478d-8d42-560abd5f4ff1","resolution":{"observed_at":"2026-08-11T15:03:56.961502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.330756Z","title":"Net- work traffic obfuscation: An adversarial machine learning a pproach,","venue":null,"work_id":"1ea9bd56-505d-42dd-9c78-9444b72285a4","year":2018},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.231774Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:cbc3daacdd412567edb88c4483549405631e71932ced7af2c9baf53b1b363638","observation_id":"7218db5a-9250-4dfd-b31b-696e96cd153e","resolution":{"observed_at":"2026-08-11T15:03:57.335699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.348036Z","title":"Black- box adversarial machine learning attack on network traffic clas sification,","venue":null,"work_id":"e647b00d-edaf-4631-9db6-a0880cb49c2b","year":2019},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.243811Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:405b85309020320e89f0f64c396e4cdc8fc2aeb7f65d1cbdb239bab2a8563f61","observation_id":"e9be8ff9-af25-4006-b3cf-a38b0357d729","resolution":{"observed_at":"2026-08-11T15:03:57.359235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:57.385686Z","title":"Mocki ngbird: Defending against deep-learning-based website fingerprin ting attacks with adversarial traces,","venue":null,"work_id":"fc362447-c457-472b-88d9-a7a7408fa86c","year":2020},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.257580Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:3cc1b2dc9dbb8afe44e259f5e0a68378af0c320794dcafe4b89cac5b1808c4a5","observation_id":"3e1ff4e4-d680-41dd-acef-02c73bd7043b","resolution":{"observed_at":"2026-08-11T15:03:57.400289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:56.898823Z","title":"Attack versus attack : Toward adversarial example defend website fingerprinting attack,","venue":null,"work_id":"fd4bcdbe-43ce-4ca2-a2c1-132660e574ca","year":2021},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.287410Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:fe59830afffc78f70da0fea7798eb05dfe3f964cab0364bbf933e07bce077a8a","observation_id":"0b3978aa-151d-4917-b66a-81c19643bd9a","resolution":{"observed_at":"2026-08-11T15:03:56.905360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:56.870921Z","title":"Adversar ial network traffic: Towards evaluating the robustness of deep-learnin g-based net- work traffic classification,","venue":null,"work_id":"5f9aed5d-6dd2-4318-908b-25793b1f89e8","year":1962},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.305659Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:4a349fb9564b76f951f706d41db264bfb1d6c7a303f5763856532f6362a88253","observation_id":"1328727f-304e-4e3c-85d6-33251b4c499a","resolution":{"observed_at":"2026-08-11T15:03:56.880711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:03:56.841705Z","title":"A survey of adversarial machine learning in c yber warfare,","venue":null,"work_id":"74321abb-ab9e-4527-ab51-3f6ed1a593d4","year":2018},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.315454Z"},"links":{"citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:0b07fb18c7647b1bc89a648b35d7d7a22fa0d08034889e47d6e4dd6eb15a8f2f","observation_id":"e35685cb-ee19-48e6-84dc-7d24845e3e0d","resolution":{"observed_at":"2026-08-11T15:03:56.850745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-08-14T22:57:08.956233Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-11T15:03:56.322009Z","title":"Distilling the know ledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.322009Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:d699b2fa07eaa7313347514773f7191e8bdfaa26a2aa5798f4e3866de2ab91b4","observation_id":"36bd5e06-6d6a-4fd9-b367-61d8ce5e9bde","resolution":{"observed_at":"2026-08-11T15:03:56.322009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.01155","last_updated":"2017-12-05T23:45:08Z","snapshot_observed_at":"2026-08-14T21:08:18.764548Z","submitted_at":"2017-04-04T18:56:53Z","title":"Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.01155","snapshot_observed_at":"2026-08-11T15:03:56.337652Z","title":"Feature squeezing: Detectin g adversarial examples in deep neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.337652Z"},"links":{"cited_paper":"/paper/1704.01155","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:15004919fff6d156ed44450ff3204143238f42821e853daf22b9c3ddf00df360","observation_id":"4b81eced-ee77-4a8b-9779-bb1ed92f1fcb","resolution":{"observed_at":"2026-08-11T15:03:56.337652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.01230","last_updated":"2025-02-23T21:18:41Z","snapshot_observed_at":"2026-08-14T07:16:37.493035Z","submitted_at":"2024-11-02T12:42:01Z","title":"Strengthening DeFi Security: A Static Analysis Approach to Flash Loan Vulnerabilities","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.01230","snapshot_observed_at":"2026-08-11T15:03:56.351701Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T15:03:56.351701Z"},"links":{"cited_paper":"/paper/2411.01230","citing_paper":"/paper/2412.12217"},"observation_digest":"sha256:1552c3332d2c9f48fdcda382c61955bd274d928b98f6c80257e6d82489c507b9","observation_id":"9d8d4417-bd6f-467d-ac51-475d46476708","resolution":{"observed_at":"2026-08-11T15:03:56.351701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.12217","last_updated":"2024-12-16T01:54:07Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-12T07:32:03.186590Z","submitted_at":"2024-12-16T01:54:07Z","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":47},"total_outbound_references":56},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2412.12217."}