{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5TVTPHTJX6VUWWV46K2DA4RWEU","short_pith_number":"pith:5TVTPHTJ","schema_version":"1.0","canonical_sha256":"eceb379e69bfab4b5abcf2b430723625106a25330d3a548e9347007d8addcc41","source":{"kind":"arxiv","id":"1911.02621","version":3},"attestation_state":"computed","paper":{"title":"The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","cs.NI"],"primary_cat":"cs.CR","authors_text":"Ashraf Matrawy, Mohamed el Shehaby, M. Omair Shafiq, Olakunle Ibitoye, Rana Abou-Khamis","submitted_at":"2019-11-06T20:29:56Z","abstract_excerpt":"Machine learning models have made many decision support systems to be faster, more accurate, and more efficient. However, applications of machine learning in network security face a more disproportionate threat of active adversarial attacks compared to other domains. This is because machine learning applications in network security such as malware detection, intrusion detection, and spam filtering are by themselves adversarial in nature. In what could be considered an arm's race between attackers and defenders, adversaries constantly probe machine learning systems with inputs that are explicit"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1911.02621","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2019-11-06T20:29:56Z","cross_cats_sorted":["cs.LG","cs.NI"],"title_canon_sha256":"52d09be013501981c9e267f9202745837101733840e6935a73e6e6215b9e3548","abstract_canon_sha256":"adc9ca10034f6a619b08b1fe52f848b720c14d56cb3c267551d9e0c0c67d8d52"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:48.589241Z","signature_b64":"cq0vebd9hU6udqjrMxr5EHHL/1KrsG88IbiFmoPKtGhtEvMJYILvmcdfaIL0tRwy/8SP3KptoLwXcSWHj4sfBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eceb379e69bfab4b5abcf2b430723625106a25330d3a548e9347007d8addcc41","last_reissued_at":"2026-07-05T05:52:48.588811Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:48.588811Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","cs.NI"],"primary_cat":"cs.CR","authors_text":"Ashraf Matrawy, Mohamed el Shehaby, M. Omair Shafiq, Olakunle Ibitoye, Rana Abou-Khamis","submitted_at":"2019-11-06T20:29:56Z","abstract_excerpt":"Machine learning models have made many decision support systems to be faster, more accurate, and more efficient. However, applications of machine learning in network security face a more disproportionate threat of active adversarial attacks compared to other domains. This is because machine learning applications in network security such as malware detection, intrusion detection, and spam filtering are by themselves adversarial in nature. In what could be considered an arm's race between attackers and defenders, adversaries constantly probe machine learning systems with inputs that are explicit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.02621","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1911.02621/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1911.02621","created_at":"2026-07-05T05:52:48.588868+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.02621v3","created_at":"2026-07-05T05:52:48.588868+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.02621","created_at":"2026-07-05T05:52:48.588868+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TVTPHTJX6VU","created_at":"2026-07-05T05:52:48.588868+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TVTPHTJX6VUWWV4","created_at":"2026-07-05T05:52:48.588868+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TVTPHTJ","created_at":"2026-07-05T05:52:48.588868+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.15552","citing_title":"Feature-level analysis and adversarial transfer in rotationally equivariant quantum machine learning","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU","json":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU.json","graph_json":"https://pith.science/api/pith-number/5TVTPHTJX6VUWWV46K2DA4RWEU/graph.json","events_json":"https://pith.science/api/pith-number/5TVTPHTJX6VUWWV46K2DA4RWEU/events.json","paper":"https://pith.science/paper/5TVTPHTJ"},"agent_actions":{"view_html":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU","download_json":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU.json","view_paper":"https://pith.science/paper/5TVTPHTJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.02621&json=true","fetch_graph":"https://pith.science/api/pith-number/5TVTPHTJX6VUWWV46K2DA4RWEU/graph.json","fetch_events":"https://pith.science/api/pith-number/5TVTPHTJX6VUWWV46K2DA4RWEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU/action/storage_attestation","attest_author":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU/action/author_attestation","sign_citation":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU/action/citation_signature","submit_replication":"https://pith.science/pith/5TVTPHTJX6VUWWV46K2DA4RWEU/action/replication_record"}},"created_at":"2026-07-05T05:52:48.588868+00:00","updated_at":"2026-07-05T05:52:48.588868+00:00"}