{"as_of":"2026-08-17T04:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bcc1e7bf1e0ed34467f4cd830dc035c49a2c25277b6f07050dcf84e6a45dd107","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:59:40.506425Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2504.14122/citation-record","integrity":"/paper/2504.14122/integrity","json":"/paper/2504.14122/citation-record.json","paper":"/paper/2504.14122"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T11:59:41.274250Z","title":"Zero-day attack detection: a systematic literature review","venue":null,"work_id":"f1d92868-041b-4867-98c5-dda1907b7d45","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.268960Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:7bb1b36370e93e92538c0d22ff528856dda1cf2d382e077f73695f5cd9cbb656","observation_id":"ed5fe2c9-6aed-4ecc-a029-a59d7641d78a","resolution":{"observed_at":"2026-08-16T11:59:41.279402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.261382Z","title":"Deep learning technique-enabled web application firewall for the detection of web attacks","venue":null,"work_id":"7e32c5fb-4f6e-4d2d-ac24-7ca2f9c0be07","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.274054Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:98a1af8c768718aab4e1f7e930c5d978237301939869ce519d70bc303832fbcb","observation_id":"1149cfc3-dc4f-4006-bedc-503d567a3e5b","resolution":{"observed_at":"2026-08-16T11:59:41.265683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.247039Z","title":"A systematic literature review of information security in chatbots.Applied Sciences 2023, 13, 6355","venue":null,"work_id":"8be1e9e3-83aa-4e20-bac0-eaad7d301bf6","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.278619Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:5f052328b5de710b96edccce8dccae24553b3c22e421bf25a7918679bae7900c","observation_id":"eb8d4ca1-27e9-4d8a-879d-6a135d2dbdf3","resolution":{"observed_at":"2026-08-16T11:59:41.251858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.234112Z","title":"Machine learning for web vulnerability detection: the case of cross-site request forgery","venue":null,"work_id":"700e9480-4bac-4e3a-9e61-0eaa9b732561","year":2020},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.283372Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:c6dd3b818615eed8a9ff44eee43543f3bfc871b4ca661549ba7e2c46d1d73918","observation_id":"810c09e4-f0ed-44dc-b61a-2709fdd77b2e","resolution":{"observed_at":"2026-08-16T11:59:41.238235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.220087Z","title":"Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection","venue":null,"work_id":"97ef0d18-4032-4d8a-b0d9-a61a8d79cc34","year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.288172Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:b295d1af4339ceb5995209d8e64d2ca44d7f27f99815daa3e35fbc9bfa6cc5f4","observation_id":"2c8fd990-6f7f-4f87-ac26-ba6e275d8b31","resolution":{"observed_at":"2026-08-16T11:59:41.224379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.206233Z","title":"A survey of network anomaly detection techniques","venue":null,"work_id":"cf1218d3-d4ea-49d4-b9e3-6bfe0c72e2e3","year":2016},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.292847Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:4818ee85fe8578618aa3d5c6cd6bf61cf5db76f6f271a60c82b2d2cd53f8a150","observation_id":"ee986488-ea67-4884-8621-df3a67737d20","resolution":{"observed_at":"2026-08-16T11:59:41.211017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.192374Z","title":"A hybrid unsupervised clustering-based anomaly detection method","venue":null,"work_id":"54c126f0-35d7-4c33-893b-7c87144d16e3","year":2020},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.297462Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:9714306fde1853824b4611acc1cb01d3c2e3c3fe336fca0cf2f56ab9268d5f9f","observation_id":"cb37af0e-7dc9-4021-9ac6-b6106ac5fbc0","resolution":{"observed_at":"2026-08-16T11:59:41.196726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.178562Z","title":"An efficient algorithm and tool for detecting dangerous website vulnerabilities","venue":null,"work_id":"d157f5a3-6477-4546-88f2-b12b8356f49c","year":2020},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.302583Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:9dcfbfb685d927860385eaaec49ab928e68049545d81e918a47ea14bcdfdf428","observation_id":"ae46d9e4-4dd4-4db7-a09e-c29c994bc520","resolution":{"observed_at":"2026-08-16T11:59:41.183099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.164662Z","title":"Learning DFA representations of HTTP for protecting web applications.Computer Networks 2007, 51, 1239–1255","venue":null,"work_id":"24ed7b1b-dce4-4f05-866a-04c31bdfb6a9","year":2007},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.306801Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:e7ad16ff7e14c1190c83686c12175737d8472be81ce2b26833999325df05c8c2","observation_id":"e5f4cf8b-0a4b-4579-8cd0-e135f36cb925","resolution":{"observed_at":"2026-08-16T11:59:41.168983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.150668Z","title":"Web intrusion detection using character level machine learning approaches with upsampled data","venue":null,"work_id":"9abd5584-69b3-4c8f-bd86-9671c087ce8c","year":2022},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.311816Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:55ccc9b23e7bf24f2c74b892d5d917bae270da4191356bc2183b0f917fe67b99","observation_id":"a215e952-86a4-4d70-b031-03255911ee63","resolution":{"observed_at":"2026-08-16T11:59:41.155634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.135403Z","title":"PF-TL: Payload feature-based transfer learning for dealing with the lack of training data","venue":null,"work_id":"43c61cb9-c4d6-4145-a6f9-10a2ba0b597b","year":2021},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.316133Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:36e3cd1546b29592409d9873b28686357da1c62cfde3eb5b7e979822bc34a2a1","observation_id":"d8a88767-6724-4929-9cc9-fa4357efff10","resolution":{"observed_at":"2026-08-16T11:59:41.140852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.120759Z","title":"An anomaly detection method to detect web attacks using stacked auto-encoder","venue":null,"work_id":"d9c98c85-4e42-4520-af6b-d2d91bc518f4","year":2018},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.321153Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:a3f92567fc8337e11f5fead5b4acb74a42878bdae4bdcfc098a2b4bc62ada517","observation_id":"7b5d732d-629e-40fd-a5cc-94ce4e82f79c","resolution":{"observed_at":"2026-08-16T11:59:41.126023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.107720Z","title":"HMMPayl: An intrusion detection system based on Hidden Markov Models","venue":null,"work_id":"ba32bbcd-9d9b-49d2-9f6d-13368ac7c8d2","year":2011},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.325667Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:e25a34cf6531369571c00732701ab833107f413c6f45727dc6b603afdea0ad73","observation_id":"ae4af4fc-5b56-4ef9-8a0d-0a84edb1714c","resolution":{"observed_at":"2026-08-16T11:59:41.112043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.094072Z","title":"Anomaly-based web attack detection: a deep learning approach","venue":null,"work_id":"1b43e3c1-f087-4685-9e88-e0e81efcde0e","year":2017},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.330703Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:0750dcd8c90f859c49f25399374b0d42777366b4ce15d7ba6ea4a8f5fa3b933b","observation_id":"e9207058-880a-43a6-be9e-1b9ed588ee9e","resolution":{"observed_at":"2026-08-16T11:59:41.098623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.079661Z","title":"DeepWAF: detecting web attacks based on CNN and LSTM models","venue":null,"work_id":"6d3c26ce-39a0-4830-b955-08e2f57b2d34","year":2019},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.334810Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:cb8fe3a59ffef9d81e26cece439582873fd9dbe7de437c54f150b4916cdc2bd6","observation_id":"76886078-a362-470a-9be7-3de7ebb114d1","resolution":{"observed_at":"2026-08-16T11:59:41.084210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.065743Z","title":"Zerowall: Detecting zero-day web attacks through encoder-decoder recurrent neural networks","venue":null,"work_id":"c4589a38-b018-4a5d-9905-1c2edb66bdb0","year":2020},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.339794Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:f7e9070ae9f4db38dda41d49521d32565841ff9a02835b057c70f62b82524948","observation_id":"8a5a221b-1f0f-4841-ba04-3e6ab55ae4ca","resolution":{"observed_at":"2026-08-16T11:59:41.070286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.052146Z","title":"Robust ensemble machine learning model for filtering phishing URLs: Expandable random gradient stacked voting classifier (ERG-SVC)","venue":null,"work_id":"000bdb55-e2da-40c1-9e33-a2fa4caee38a","year":2021},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.344234Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:489d38627537c80d45bbdd269f408bc6d65a56c39de39b531f7c43cf9cfab8ef","observation_id":"8f7f9181-9601-459e-be94-8ad845d32f76","resolution":{"observed_at":"2026-08-16T11:59:41.056507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.037807Z","title":"Model uncertainty based annotation error fixing for web attack detection","venue":null,"work_id":"dc23e0e4-4713-4261-8067-6cac98434e15","year":2021},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.349005Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:3e1d3f34aa7fb3a72b95804a5cc400ebf00c1d9ac5536bd6d77c0f4434383608","observation_id":"13c608bf-f3a0-424d-b897-44f6530e9e6e","resolution":{"observed_at":"2026-08-16T11:59:41.043165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.023227Z","title":"A novel architecture for web-based attack detection using convolutional neural network","venue":null,"work_id":"0a967672-626f-450f-98ad-f50d63d47edc","year":2021},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.353683Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:4bbdbf4b0e83c1064a9eaae1dfe1efa28d9799264975437501e621f581e48ef0","observation_id":"82f38b18-63f0-4acc-a520-2e26ae132317","resolution":{"observed_at":"2026-08-16T11:59:41.028864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:41.008881Z","title":"SWAF: a smart web application firewall based on convolutional neural network","venue":null,"work_id":"26461a3b-fe5c-4699-967d-60459235d29d","year":2022},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.358506Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:d999946765bd94f85b18bca793fa471e737400e1bcb9e2f9c02298a199e26da3","observation_id":"459b84a3-039c-4495-ae78-91020455d599","resolution":{"observed_at":"2026-08-16T11:59:41.013700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.994768Z","title":"Web attacks detection using stacked generalization ensemble for LSTMs and word embedding","venue":null,"work_id":"5b804267-5e00-45fd-9f21-137305e2dfbb","year":2022},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.363031Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:bf16cbfe24042f7a8728c865630d22c29d560ae4f81556b0d957d84f055d02d2","observation_id":"4b519a20-b4fb-488a-af22-c5bc37b68bef","resolution":{"observed_at":"2026-08-16T11:59:40.999817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.980770Z","title":"MC-MLDCNN: Multichannel Multilayer Dilated Convolutional Neural Networks for Web Attack Detection","venue":null,"work_id":"a242d3a6-8e1c-4815-a9da-abda0e63ef85","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.367968Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:dde3907020dafe304168fb585a41734a9f8bf2242c44bcfc3230d958123ee93e","observation_id":"9414dec6-4161-4004-b44f-8b476987efb3","resolution":{"observed_at":"2026-08-16T11:59:40.985420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.966609Z","title":"A Static Detection Method for SQL Injection Vulnerability Based on Program Transformation","venue":null,"work_id":"7a79fa26-653c-4d35-bbc3-2dfe6ff85fa6","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.372158Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:23dcb862956b3474dd8c13a90dd1234c83c1cc74c7ba7b44e600c90fe671640f","observation_id":"2938f770-2ad2-4f34-a730-c1609a35f62c","resolution":{"observed_at":"2026-08-16T11:59:40.971225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.952742Z","title":"Synthesis of Allowlists for Runtime Protection against SQLi","venue":null,"work_id":"0b168e39-67ca-49ee-9051-1cb2d278bf19","year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.377158Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:21274aeb03a6565b4fe82e49a8653bb0db787b6392eb5fc393f207306ade4405","observation_id":"f8a7a0fc-6d45-46e5-987c-c42c4409211c","resolution":{"observed_at":"2026-08-16T11:59:40.957309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.939166Z","title":"Splendor: Static Detection of Stored XSS in Modern Web Applications","venue":null,"work_id":"6033c314-5852-4339-8499-8c3740dd1be4","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.382264Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:df6ac4d7e92cc0478b22576eef279d965d178e6340fd5184d9912dfe8e7012a5","observation_id":"c68b3c4e-08f2-4546-8f14-669e20b9af26","resolution":{"observed_at":"2026-08-16T11:59:40.943477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.924944Z","title":"Towards a SQL Injection Vulnerability Detector Based on Session Types","venue":null,"work_id":"0069ae31-6fd1-4b8c-b54f-10cef713a76a","year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.387342Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:233a184a41bb3453b514b7b93815ca564f470aa2ecade528498b67aac3cd3697","observation_id":"4813dcaa-2aa1-4465-bbfd-8e7b14c8dce6","resolution":{"observed_at":"2026-08-16T11:59:40.929786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.910670Z","title":"Towards a Zero-Day Anomaly Detector in Cyber Physical Systems Using a Hybrid VAE-LSTM-OCSVM Model","venue":null,"work_id":"7f81a931-57e4-4b18-b718-69608f2b346f","year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.391643Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:5670d5dcb9367434500234b6715fa0d1f893c10595963c976bd731714ce755e2","observation_id":"3d6a4c2e-6115-48df-b5f4-87e019e9ee4f","resolution":{"observed_at":"2026-08-16T11:59:40.915505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.896684Z","title":"One-class IoT anomaly detection system using an improved interpolated deep SVDD autoencoder with adversarial regularizer","venue":null,"work_id":"0be99d4d-36ed-4751-8f81-2f615904ec94","year":2025},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.396697Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:fb9888b674e378ddfae0102a7d60c93656b9659dae264ccf7efc7f083945bc33","observation_id":"bc56b58e-d762-47ec-a5c3-c1dfca8574c2","resolution":{"observed_at":"2026-08-16T11:59:40.901347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00304","last_updated":"2023-11-01T05:29:42Z","snapshot_observed_at":"2026-08-16T14:46:58.606897Z","submitted_at":"2023-11-01T05:29:42Z","title":"Stacking an autoencoder for feature selection of zero-day threats","version":1},"cited_work":{"arxiv_id":"2311.00304","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.00304","snapshot_observed_at":"2026-08-16T11:59:40.652728Z","title":"Stacking an autoencoder for feature selection of zero-day threats","venue":"cs.CR","work_id":"cc3d235f-311b-41d1-a61a-2cf42a16f35e","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.401314Z"},"links":{"cited_paper":"/paper/2311.00304","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:4e7df6458cf7427cd7a47baf322948ccaab8b082267ca67c16722e69c68944b9","observation_id":"a1ecb1b4-1d97-480a-9525-ec3f6b11cff6","resolution":{"observed_at":"2026-08-16T11:59:40.657486Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.882566Z","title":"Deep learning architecture for detecting SQL injection attacks based on RNN autoencoder model","venue":null,"work_id":"0b5c4d66-0585-4405-8254-3be217fa46b3","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.406668Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:bd51dbdda97ca1befc7d51fac669e4b6060f085a184348565dbf24cb784b3aed","observation_id":"a2dd98ab-3581-4766-8d5f-a37bb2cba92d","resolution":{"observed_at":"2026-08-16T11:59:40.887492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.867375Z","title":"Ae-net: Novel autoencoder-based deep features for sql injection attack detection","venue":null,"work_id":"bd337c5a-bebe-4bf8-b975-71278aba6951","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.410959Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:95dd085a1a1ec972b71a0c88ad0e044082fc8c4559153d8f3f697cbe5b185517","observation_id":"d801f392-afb4-4a20-ac76-b0acdc373ba7","resolution":{"observed_at":"2026-08-16T11:59:40.872605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.853253Z","title":"A lightweight intelligent network intrusion detection system using one-class autoencoder and ensemble learning for IoT","venue":null,"work_id":"d3a403cc-f779-48bf-ae49-a23e9847cb3c","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.415268Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:011e513c9e3b9dc547a094a4911dd8afb9a45c49a9cfe2eead49e3137111e63a","observation_id":"7c1e7fca-66db-4f3c-9826-cb4072db03fc","resolution":{"observed_at":"2026-08-16T11:59:40.858506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.839629Z","title":"Multi-Class Intrusion Detection System using Deep Learning","venue":null,"work_id":"c2a65340-5e42-4192-bf1f-d06feb3170a3","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.420342Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:899d5c7b366aca8c9678920011aa9540ba98854441757dbe1a5b3c14e5e59667","observation_id":"4404cb50-fbf9-4a22-8bfa-3be9f5ea22ef","resolution":{"observed_at":"2026-08-16T11:59:40.844042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.824799Z","title":"An enhanced deep learning based framework for web attacks detection, mitigation and attacker profiling","venue":null,"work_id":"206f0b42-b2fe-461a-93ff-527e0ebd52bc","year":2022},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.424562Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:9925912f86c2b59eeaec3ad5b23f6a5e3a4b0c81ea99f397d4e860f36e896197","observation_id":"20b0bb7e-9adc-4227-9ea7-acbfd67ef474","resolution":{"observed_at":"2026-08-16T11:59:40.829329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.811202Z","title":"Dynamic defenses and the transferability of adversarial examples","venue":null,"work_id":"7c511532-2015-4720-92a9-60833ad51172","year":2022},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.428548Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:a38a23bd43f7d684e33121b36a5bbbab9fd95edda3f5c3401d3e28870e61fd5b","observation_id":"b1dd6cc3-c581-49ae-b5fd-f4896cfeb0fb","resolution":{"observed_at":"2026-08-16T11:59:40.815714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.797508Z","title":"Predicting web vulnerabilities in web applications based on machine learning","venue":null,"work_id":"0377b178-4616-41d8-97f3-b2bd23888d02","year":2018},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.433487Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:0a71d2fe5ef3a223cafbcec90586c397efa7b6c4f439da3fae95fe5428fd77a6","observation_id":"0d205af5-2248-494d-aadb-e86e4ebcd2e3","resolution":{"observed_at":"2026-08-16T11:59:40.802146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.783575Z","title":"Learning web request patterns","venue":null,"work_id":"99079ee4-fbc9-4fb1-96ec-fe96a566f32f","year":2004},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.437874Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:02f96289d47f599808abe522e3ef71a83d75699a4967a9b2ae2ebad3c06e0b7e","observation_id":"936c8df5-76bf-4ace-a774-3adadd84b0d0","resolution":{"observed_at":"2026-08-16T11:59:40.788349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.770136Z","title":"Text mining: open source tokenization tools-an analysis","venue":null,"work_id":"5429fe19-9fc4-4495-846b-f7c83854bc0d","year":2016},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.442535Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:da181d197e060e404a7e0199d9bd956cf4eafce83b1024e38a87553be2e3a7f8","observation_id":"6aee187a-8da9-4478-935b-8a08c7900cd7","resolution":{"observed_at":"2026-08-16T11:59:40.774511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.15495","last_updated":"2024-03-04T15:42:31Z","snapshot_observed_at":"2026-08-16T15:44:59.762285Z","submitted_at":"2023-03-27T16:45:22Z","title":"Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.15495","snapshot_observed_at":"2026-08-16T11:59:40.446669Z","title":"Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.446669Z"},"links":{"cited_paper":"/paper/2303.15495","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:3cd89cc6e45886e48248f0d6dc60b9ffd3816dc82c777784ea906b8e3f4d3e54","observation_id":"46694e4d-8bc4-49fc-aa28-95d90bc2e5aa","resolution":{"observed_at":"2026-08-16T11:59:40.446669Z","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":"2026-08-16T11:59:40.755459Z","title":"Attention is all you need","venue":null,"work_id":"49f8440b-1727-4bf9-947f-9e567f57c1bd","year":2017},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.452002Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:5dae66dfbca7b5a59df69a6b92cd81d145376071ff30abf19af04464b9d56830","observation_id":"45852fea-82df-45b2-bbf0-066ebf45feb8","resolution":{"observed_at":"2026-08-16T11:59:40.760655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.741352Z","title":"Enhancing automatic modulation recognition for iot applications using transformers","venue":null,"work_id":"bcdea20c-f833-45a7-bfe6-b1003a65f767","year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.455925Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:a98147634175bbe7c2b459464155fc0219bb50d23b79e3aeb7fc24431b43f4ec","observation_id":"91163643-2196-4568-b548-338c5d13606b","resolution":{"observed_at":"2026-08-16T11:59:40.746231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.725081Z","title":"Web application firewall using machine learning and features engineering","venue":null,"work_id":"ff3346cd-ee74-4303-a1f4-a234385cc3aa","year":2022},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.460047Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:cc17a9b8d55bae3a36f2f4245c528638b680d2c918670e7876b3c278ecf37d6d","observation_id":"e83e40e2-014f-4bd3-9c16-d5e7a67626a4","resolution":{"observed_at":"2026-08-16T11:59:40.729993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.710743Z","title":"CNN Web Application Firewall","venue":null,"work_id":"a4caff9f-b254-4187-8f8a-ef5ac540a1a1","year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.464097Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:e968732038659cc860b9065c753df94182f03373b2052987302f64ed7dfe9b81","observation_id":"770dd0db-3ef8-4874-a81f-976fe03b13de","resolution":{"observed_at":"2026-08-16T11:59:40.714998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:59:40.696783Z","title":"Detecting web attacks from HTTP weblogs using variational LSTM autoencoder deviation network","venue":null,"work_id":"cddfbd31-a130-418d-bde7-3b5b6f952997","year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.468420Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:1c4905e0515cb28f71ee3dbebed8defdb90ab09146a48e3f78306748e095b95d","observation_id":"a4d744fa-c454-40e8-a882-3f39bb1d83cf","resolution":{"observed_at":"2026-08-16T11:59:40.701378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.10918","last_updated":"2025-06-02T16:27:40Z","snapshot_observed_at":"2026-08-16T02:45:16.473597Z","submitted_at":"2024-11-17T00:09:04Z","title":"INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.10918","snapshot_observed_at":"2026-08-16T11:59:40.473768Z","title":"LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.473768Z"},"links":{"cited_paper":"/paper/2411.10918","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:f733321f572e04262aa8bd79ffb4285e821f606f5b588c9b28d9c01c39f2c4ea","observation_id":"6c339477-df82-45e2-af8e-05e654fa731c","resolution":{"observed_at":"2026-08-16T11:59:40.473768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.07966","last_updated":"2024-07-10T18:03:24Z","snapshot_observed_at":"2026-08-16T13:35:19.139988Z","submitted_at":"2024-07-10T18:03:24Z","title":"A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.07966","snapshot_observed_at":"2026-08-16T11:59:40.478485Z","title":"A comprehensive survey on the security of smart grid: Challenges, mitigations, and future research opportunities","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.478485Z"},"links":{"cited_paper":"/paper/2407.07966","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:8ebf354429e1b78f1ef33811369ac5b8a2a7b4d2a282a8f4988a561635d6d1a3","observation_id":"7fd004f0-385f-42f1-80e5-b21a3d08ea57","resolution":{"observed_at":"2026-08-16T11:59:40.478485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13256","last_updated":"2026-04-06T13:44:11Z","snapshot_observed_at":"2026-07-06T20:38:53.272601Z","submitted_at":"2025-02-18T19:38:18Z","title":"Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13256","snapshot_observed_at":"2026-08-16T11:59:40.483761Z","title":"A Survey of Anomaly Detection in Cyber-Physical Systems","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.483761Z"},"links":{"cited_paper":"/paper/2502.13256","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:4ae532bd39a6d3a2840570725bd2c36d52c101108258670dd89157a00db9c1fb","observation_id":"096da3b6-7c91-48dd-a52e-cedb2af1dbef","resolution":{"observed_at":"2026-08-16T11:59:40.483761Z","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":"2026-08-16T11:59:40.681609Z","title":"GenSQLi: A Generative Artificial Intelligence Framework for Automatically Securing Web Application Firewalls Against Structured Query Language Injection Attacks","venue":null,"work_id":"809ae139-4097-452c-a874-7f38c4f3a06f","year":2025},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.488234Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:7271a42938362270bcb269f79279427df3d7fc8b8d3023cada88a1708e01d764","observation_id":"9c6e8dfb-8fb3-4459-a02e-43df60c329fb","resolution":{"observed_at":"2026-08-16T11:59:40.686856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.08176","last_updated":"2025-04-11T00:13:59Z","snapshot_observed_at":"2026-08-16T12:42:10.990513Z","submitted_at":"2025-04-11T00:13:59Z","title":"GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs","version":1},"cited_work":{"arxiv_id":"2504.08176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.08176","snapshot_observed_at":"2026-08-16T11:59:40.572552Z","title":"GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs","venue":"cs.CR","work_id":"57b029eb-f78e-40e3-b0f2-bcd42da1637c","year":2025},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.492462Z"},"links":{"cited_paper":"/paper/2504.08176","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:cc9271b338dc4cb51c49a6a02b4e5c8368c39efe99db04fe847d23192b6a8259","observation_id":"f8cba526-6e53-430b-887f-03092365f6d8","resolution":{"observed_at":"2026-08-16T11:59:40.580111Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.11382","last_updated":"2023-02-21T12:42:44Z","snapshot_observed_at":"2026-07-06T14:54:37.559648Z","submitted_at":"2023-02-21T12:42:44Z","title":"A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.11382","snapshot_observed_at":"2026-08-16T11:59:40.497690Z","title":"A prompt pattern catalog to enhance prompt engineering with chatgpt","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.497690Z"},"links":{"cited_paper":"/paper/2302.11382","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:b2755db3507f0f5f85c5e6103ddad61e7ef2b58ef6102f5a4a7f7c9b31382d46","observation_id":"9b776fcd-3624-4ab5-a008-62c0d5a06d5d","resolution":{"observed_at":"2026-08-16T11:59:40.497690Z","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":"2026-08-16T11:59:40.668122Z","title":"Hybrid speech recognition with deep bidirectional LSTM","venue":null,"work_id":"28e7b253-e8d8-4770-b01b-024ffb0701c2","year":2013},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.502161Z"},"links":{"citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:d48ec4b0933146139d62f8b84f93fb315aa1241507a0f7b5ac24661c0e238dbf","observation_id":"f0918c48-a86e-49a6-9c0a-8633076f47e6","resolution":{"observed_at":"2026-08-16T11:59:40.672576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.17885","last_updated":"2025-03-22T23:59:17Z","snapshot_observed_at":"2026-08-16T12:47:41.745733Z","submitted_at":"2025-03-22T23:59:17Z","title":"Reasoning with LLMs for Zero-Shot Vulnerability Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.17885","snapshot_observed_at":"2026-08-16T11:59:40.506425Z","title":"Reasoning with LLMs for Zero-Shot Vulnerability Detection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:40.506425Z"},"links":{"cited_paper":"/paper/2503.17885","citing_paper":"/paper/2504.14122"},"observation_digest":"sha256:f34bd4770c830d27fa41a30f357c93249f221f1e072cf0892c54da57306a65b4","observation_id":"8142da95-ca34-4fc2-b704-d1332b45f164","resolution":{"observed_at":"2026-08-16T11:59:40.506425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.14122","last_updated":"2025-04-19T00:48:00Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-16T11:54:01.228354Z","submitted_at":"2025-04-19T00:48:00Z","title":"Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":2,"verified_fuzzy":44},"total_outbound_references":52},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2504.14122."}