{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HR4D5XNSMDM42P3X55OJKWF554","short_pith_number":"pith:HR4D5XNS","schema_version":"1.0","canonical_sha256":"3c783eddb260d9cd3f77ef5c9558bdef3614686fa342329e8defab50e7850404","source":{"kind":"arxiv","id":"2111.13978","version":1},"attestation_state":"computed","paper":{"title":"Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Hooman Alavizadeh, Hootan Alavizadeh, Julian Jang-Jaccard","submitted_at":"2021-11-27T20:18:00Z","abstract_excerpt":"The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of network intrusion detection methods that combines a Q-learning-based reinforcement learning with a deep-feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learnin"},"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":"2111.13978","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2021-11-27T20:18:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4dacfbb76d6c1229887c1bc1bc27dd9a2a429360abe47169c31fc49e938735d9","abstract_canon_sha256":"327c9e036a25a18ef17302511cba38cc01b597d6565fabc880e928a9880acbc8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:35:37.848450Z","signature_b64":"sid8N+N22B934iu7G7C/sReKysUdKbiKYkYLl9PFGlbFzIYTedF0MVIr1zwZQ/kFspT+hjwCYMA/mEN4w+YQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c783eddb260d9cd3f77ef5c9558bdef3614686fa342329e8defab50e7850404","last_reissued_at":"2026-07-05T03:35:37.848051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:35:37.848051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Hooman Alavizadeh, Hootan Alavizadeh, Julian Jang-Jaccard","submitted_at":"2021-11-27T20:18:00Z","abstract_excerpt":"The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of network intrusion detection methods that combines a Q-learning-based reinforcement learning with a deep-feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learnin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.13978","kind":"arxiv","version":1},"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/2111.13978/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":"2111.13978","created_at":"2026-07-05T03:35:37.848106+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.13978v1","created_at":"2026-07-05T03:35:37.848106+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.13978","created_at":"2026-07-05T03:35:37.848106+00:00"},{"alias_kind":"pith_short_12","alias_value":"HR4D5XNSMDM4","created_at":"2026-07-05T03:35:37.848106+00:00"},{"alias_kind":"pith_short_16","alias_value":"HR4D5XNSMDM42P3X","created_at":"2026-07-05T03:35:37.848106+00:00"},{"alias_kind":"pith_short_8","alias_value":"HR4D5XNS","created_at":"2026-07-05T03:35:37.848106+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.19837","citing_title":"Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications","ref_index":2021,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554","json":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554.json","graph_json":"https://pith.science/api/pith-number/HR4D5XNSMDM42P3X55OJKWF554/graph.json","events_json":"https://pith.science/api/pith-number/HR4D5XNSMDM42P3X55OJKWF554/events.json","paper":"https://pith.science/paper/HR4D5XNS"},"agent_actions":{"view_html":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554","download_json":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554.json","view_paper":"https://pith.science/paper/HR4D5XNS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.13978&json=true","fetch_graph":"https://pith.science/api/pith-number/HR4D5XNSMDM42P3X55OJKWF554/graph.json","fetch_events":"https://pith.science/api/pith-number/HR4D5XNSMDM42P3X55OJKWF554/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554/action/storage_attestation","attest_author":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554/action/author_attestation","sign_citation":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554/action/citation_signature","submit_replication":"https://pith.science/pith/HR4D5XNSMDM42P3X55OJKWF554/action/replication_record"}},"created_at":"2026-07-05T03:35:37.848106+00:00","updated_at":"2026-07-05T03:35:37.848106+00:00"}