{"as_of":"2026-08-10T12:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f7ee22b94b1efc3dbf795c45d745cd45fa31a75953ba97c8ac9fe3ffe8948fbb","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T04:30:48.468417Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2608.03642/citation-record","integrity":"/paper/2608.03642/integrity","json":"/paper/2608.03642/citation-record.json","paper":"/paper/2608.03642"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.407682Z","title":"Androzoo: Collecting millions of android apps for the research community,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.407682Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:14e46408cbdf00b0a558fd295dbbca258160b5bd02103a791c9793e5479b6416","observation_id":"5fb3c3ea-f1a6-485a-9d0e-3e50437c2a62","resolution":{"observed_at":"2026-08-10T04:30:48.407682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.04637","last_updated":"2018-04-16T20:43:33Z","snapshot_observed_at":"2026-07-06T06:33:11.138971Z","submitted_at":"2018-04-12T17:23:56Z","title":"EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.04637","snapshot_observed_at":"2026-08-10T04:30:48.411551Z","title":"Ember: an open dataset for training static pe mal- ware machine learning models,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.411551Z"},"links":{"cited_paper":"/paper/1804.04637","citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:404b1d197d4cb018a41ef30faf15a9aab3f1c472fe7dd31671b77d3199bcdf0b","observation_id":"4afe40a6-22d0-43f6-9848-7dae77ed911b","resolution":{"observed_at":"2026-08-10T04:30:48.411551Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.415437Z","title":"Drebin: Effective and explainable detection of android malware in your pocket","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.415437Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:4b538c0ae205760be73e3b3bf3ffb41fbd67c133777282bd1fabd91769f39260","observation_id":"b4e8bf34-08b4-46c8-8c68-b5a7d034f205","resolution":{"observed_at":"2026-08-10T04:30:48.415437Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.418531Z","title":"Transcending transcend: Revisiting malware classification in the presence of concept drift,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.418531Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:11d9712e914a29c4946471004a7a6f2e7c8a5637b0d9032f9f2ddd8e95475632","observation_id":"a79f565f-6e12-4894-aa5b-f8058d4f5cf0","resolution":{"observed_at":"2026-08-10T04:30:48.418531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.07860","last_updated":"2021-06-15T03:31:02Z","snapshot_observed_at":"2026-08-08T10:06:06.820131Z","submitted_at":"2021-06-15T03:31:02Z","title":"Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.07860","snapshot_observed_at":"2026-08-10T04:30:48.422151Z","title":"Evad- ing malware classifiers via monte carlo mutant feature discovery,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.422151Z"},"links":{"cited_paper":"/paper/2106.07860","citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:545e1d2881c354635d36d4cf060f84891cddcf5fdf5a3333fa5aeea1129a575c","observation_id":"6d5e4e80-5e84-4f2d-996b-7dcf0fced74e","resolution":{"observed_at":"2026-08-10T04:30:48.422151Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.425686Z","title":"Continuous learning for android malware detection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.425686Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:a4937d988df103159bf374a385f8bb415c90f6a04fcf1be7ce76962ed79e1b9f","observation_id":"e187ae5a-112a-4be9-ab93-e9fb2dc778c1","resolution":{"observed_at":"2026-08-10T04:30:48.425686Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.429084Z","title":"Adversarial exem- ples: Functionality-preserving optimization of adversarial windows malware,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.429084Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:4b21af565fc64abb140b451ebf6611979b36854f31bea908becd73357feb3738","observation_id":"9e751dcd-eec7-441c-9430-56d53c28e0c5","resolution":{"observed_at":"2026-08-10T04:30:48.429084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.03583","last_updated":"2019-01-24T08:04:43Z","snapshot_observed_at":"2026-08-01T10:39:27.995672Z","submitted_at":"2019-01-11T14:02:51Z","title":"Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.03583","snapshot_observed_at":"2026-08-10T04:30:48.432278Z","title":"Explaining vul- nerabilities of deep learning to adversarial malware binaries,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.432278Z"},"links":{"cited_paper":"/paper/1901.03583","citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:82e1366311ba3a72df6cc656d9cba408e2ec77f67c5fe917261cda76a352f56a","observation_id":"185da30d-342b-480c-ac49-1b116852520b","resolution":{"observed_at":"2026-08-10T04:30:48.432278Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.435896Z","title":"Transcend: Detecting concept drift in malware classification mod- els,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.435896Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:df0fd51b6db737b1504b00f1ec76668f313e771728f2cb93bf0d91bcd45c0207","observation_id":"cec05756-88d5-4e02-8507-a8c8165b2310","resolution":{"observed_at":"2026-08-10T04:30:48.435896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.04528","last_updated":"2019-01-10T09:21:23Z","snapshot_observed_at":"2026-08-05T16:29:23.207574Z","submitted_at":"2018-02-13T09:51:41Z","title":"Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.04528","snapshot_observed_at":"2026-08-10T04:30:48.442583Z","title":"Deceiving end-to-end deep learning malware detectors using adversarial examples,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.442583Z"},"links":{"cited_paper":"/paper/1802.04528","citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:097ec74fcd318308c34d2506fb1bf2cc62facaa07221f46b7e086d4518e526c8","observation_id":"efa8f771-6c7d-4756-b4e3-4f136c5279fa","resolution":{"observed_at":"2026-08-10T04:30:48.442583Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.445540Z","title":"Malware makeover: Breaking ml-based static analysis by modifying executable bytes,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.445540Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:a3d37a637f08fc8011f85d27834b9448d1b1aa6cf727aec8a7e0f6a0a65fa82b","observation_id":"91d5f779-e692-403e-bef3-0f501d98a45a","resolution":{"observed_at":"2026-08-10T04:30:48.445540Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.448638Z","title":"Malware detection by eating a whole exe,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.448638Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:edf0697f81439240d7f3fee678beaa6d34cb84cfe782b9152ce2c131998440c0","observation_id":"31a14ebf-a99e-43c1-9a46-fa95515cee8c","resolution":{"observed_at":"2026-08-10T04:30:48.448638Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.451669Z","title":"{Explanation-Guided}backdoor poisoning attacks against malware classifiers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.451669Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:2dd39cf98bceabc15f35a7fba02a1482fe85c77fda1856efc6834d871d638d98","observation_id":"df2d520c-75be-48f4-859a-141c79e1e7e2","resolution":{"observed_at":"2026-08-10T04:30:48.451669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03100","last_updated":"2021-04-29T21:01:53Z","snapshot_observed_at":"2026-08-05T09:55:32.636097Z","submitted_at":"2020-03-06T09:33:39Z","title":"MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03100","snapshot_observed_at":"2026-08-10T04:30:48.454670Z","title":"Automatic gener- ation of adversarial examples for interpreting malware classifiers,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.454670Z"},"links":{"cited_paper":"/paper/2003.03100","citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:2990ea89a17b5070500f1e603587417eb779f4dc872d6b68ebcac2421c3949f0","observation_id":"b19fd4f1-7fde-4351-a654-b450756b86f7","resolution":{"observed_at":"2026-08-10T04:30:48.454670Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.457826Z","title":"Exploring adversarial examples in malware detection,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.457826Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:1893ebf500c20332ad1d4298f0176169b0b5c1954476c794a102db522ad59dc9","observation_id":"8c9d3ce0-8eda-421c-b372-c2004f7be0af","resolution":{"observed_at":"2026-08-10T04:30:48.457826Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.460728Z","title":"When does machine learning{F AIL}? generalized transferability for evasion and poisoning attacks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.460728Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:9e0c55bfbbf13de511bc3a66df2f26f5e66219d3e211b6e516b02e4e624cf732","observation_id":"b152298f-b5ee-421f-a0d5-5e54b6b08865","resolution":{"observed_at":"2026-08-10T04:30:48.460728Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.463294Z","title":"Jigsaw puzzle: Selective backdoor attack to subvert malware clas- sifiers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.463294Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:b45995b7fa8688db3f5ae586be6ac031427b4b166429e0ec6d7f12c46d5d08e1","observation_id":"12e353cf-4214-42b5-8d50-a84258c5f26b","resolution":{"observed_at":"2026-08-10T04:30:48.463294Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T04:30:48.465912Z","title":"Bodmas: An open dataset for learning based temporal analysis of pe malware,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.465912Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:a18dd7d975a596a8393548aa19aa1956b72c3d18e1d67824410f1b34e948a182","observation_id":"d0f958f9-c123-4c38-9211-640e49a349db","resolution":{"observed_at":"2026-08-10T04:30:48.465912Z","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":"2026-08-10T04:30:48.545757Z","title":"{CADE}: Detecting and explaining concept drift samples for security applica- tions,","venue":null,"work_id":"d2b790a5-b5c1-41e3-89a9-b9b3990b5a43","year":2021},"citing_paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T04:30:48.468417Z"},"links":{"citing_paper":"/paper/2608.03642"},"observation_digest":"sha256:d5da9843ce87e187457a8b0490e1cfc767d981ff27652aed80b2944a191527ea","observation_id":"b64986f6-dc07-48ce-9e6c-4f55c914d9e0","resolution":{"observed_at":"2026-08-10T04:30:48.550926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.03642","last_updated":"2026-08-06T22:05:24Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-10T12:09:40.777066Z","submitted_at":"2026-08-04T13:27:57Z","title":"Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":19},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2608.03642."}