{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3524GQ6SBLZP67CESRWDN6XT2A","short_pith_number":"pith:3524GQ6S","schema_version":"1.0","canonical_sha256":"df75c343d20af2ff7c44946c36faf3d0295aad0b802b9cb44811b3d7d216123b","source":{"kind":"arxiv","id":"2409.18736","version":3},"attestation_state":"computed","paper":{"title":"Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.ET","cs.NI"],"primary_cat":"cs.CR","authors_text":"Alicia Kbidi, Dorjan Hitaj, Fabio De Gaspari, Luigi V. Mancini, Sabrine Ennaji","submitted_at":"2024-09-27T13:27:29Z","abstract_excerpt":"Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex relationships between features and effectively generalize to unseen data. Deep neural networks, in particular, contributed to this progress by enabling the analysis of large amounts of training data, significantly enhancing detection performance. However, machine learning models remain vulnerable to adversarial attacks, where carefully crafted input dat"},"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":"2409.18736","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-09-27T13:27:29Z","cross_cats_sorted":["cs.ET","cs.NI"],"title_canon_sha256":"f51bf5bd104def820b352004596a581a4668c471f8ea9cc2b98100533ce823b6","abstract_canon_sha256":"526a921c833cdd16317feac21683345b8e1d713d8868b63a980237eb6ad11306"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:59.716864Z","signature_b64":"tNShqRAZsmJR2saZc6Q5vHRlYUd38vwyG9BJFvCHocgdL1B8mWjXEzOPZySf+2fbyKryUfdOMeP/LimKYkxEBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df75c343d20af2ff7c44946c36faf3d0295aad0b802b9cb44811b3d7d216123b","last_reissued_at":"2026-07-05T09:23:59.716339Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:59.716339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.ET","cs.NI"],"primary_cat":"cs.CR","authors_text":"Alicia Kbidi, Dorjan Hitaj, Fabio De Gaspari, Luigi V. Mancini, Sabrine Ennaji","submitted_at":"2024-09-27T13:27:29Z","abstract_excerpt":"Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex relationships between features and effectively generalize to unseen data. Deep neural networks, in particular, contributed to this progress by enabling the analysis of large amounts of training data, significantly enhancing detection performance. However, machine learning models remain vulnerable to adversarial attacks, where carefully crafted input dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18736","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/2409.18736/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":"2409.18736","created_at":"2026-07-05T09:23:59.716414+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.18736v3","created_at":"2026-07-05T09:23:59.716414+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18736","created_at":"2026-07-05T09:23:59.716414+00:00"},{"alias_kind":"pith_short_12","alias_value":"3524GQ6SBLZP","created_at":"2026-07-05T09:23:59.716414+00:00"},{"alias_kind":"pith_short_16","alias_value":"3524GQ6SBLZP67CE","created_at":"2026-07-05T09:23:59.716414+00:00"},{"alias_kind":"pith_short_8","alias_value":"3524GQ6S","created_at":"2026-07-05T09:23:59.716414+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01194","citing_title":"Detecting Adversarial Evasion Attacks Against Autoencoder-Based Network Intrusion Detection Systems","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A","json":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A.json","graph_json":"https://pith.science/api/pith-number/3524GQ6SBLZP67CESRWDN6XT2A/graph.json","events_json":"https://pith.science/api/pith-number/3524GQ6SBLZP67CESRWDN6XT2A/events.json","paper":"https://pith.science/paper/3524GQ6S"},"agent_actions":{"view_html":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A","download_json":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A.json","view_paper":"https://pith.science/paper/3524GQ6S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.18736&json=true","fetch_graph":"https://pith.science/api/pith-number/3524GQ6SBLZP67CESRWDN6XT2A/graph.json","fetch_events":"https://pith.science/api/pith-number/3524GQ6SBLZP67CESRWDN6XT2A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A/action/storage_attestation","attest_author":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A/action/author_attestation","sign_citation":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A/action/citation_signature","submit_replication":"https://pith.science/pith/3524GQ6SBLZP67CESRWDN6XT2A/action/replication_record"}},"created_at":"2026-07-05T09:23:59.716414+00:00","updated_at":"2026-07-05T09:23:59.716414+00:00"}