{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:F4IY44XGLIR2PX7LRQ2BO3GMDD","short_pith_number":"pith:F4IY44XG","schema_version":"1.0","canonical_sha256":"2f118e72e65a23a7dfeb8c34176ccc18d2b453df559f4cda9e8f56e6f25e9562","source":{"kind":"arxiv","id":"2606.05844","version":1},"attestation_state":"computed","paper":{"title":"GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Ali Shoker, Hassan Jalil Hadi, Rehana Yasmin","submitted_at":"2026-06-04T08:19:52Z","abstract_excerpt":"Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets (e.g., CICIDS2017, UNSW-NB15) focus on traffic classification and provide little structured information to support automatic rule synthesis or prevention logic. To address this gap, we propose Generative Thread Intelligence (GenTI) \\footnote{GenTI refers to the proposed framework, and GTI refers to the dataset.} an LLM-driven benchmark "},"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":"2606.05844","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-06-04T08:19:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"be33267c9f68fbc794980ff2c2f6ef14b9dd4ea78e4b711bf6505b0d252eeac9","abstract_canon_sha256":"08edcde75312c5b591ea1ca56ab02d1fd9d1aa547b789887a5bbd32e6c1839c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:05.430637Z","signature_b64":"4vM4OxZBc/FvWZ093gQBwtwl2zz3zg+DnrC3lao7GguTZ6818w5L9xaTcuvGpMBW1Hh8nkztX3L8wA6A+3/DAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f118e72e65a23a7dfeb8c34176ccc18d2b453df559f4cda9e8f56e6f25e9562","last_reissued_at":"2026-06-05T01:15:05.430118Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:05.430118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Ali Shoker, Hassan Jalil Hadi, Rehana Yasmin","submitted_at":"2026-06-04T08:19:52Z","abstract_excerpt":"Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets (e.g., CICIDS2017, UNSW-NB15) focus on traffic classification and provide little structured information to support automatic rule synthesis or prevention logic. To address this gap, we propose Generative Thread Intelligence (GenTI) \\footnote{GenTI refers to the proposed framework, and GTI refers to the dataset.} an LLM-driven benchmark "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05844","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/2606.05844/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":"2606.05844","created_at":"2026-06-05T01:15:05.430185+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05844v1","created_at":"2026-06-05T01:15:05.430185+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05844","created_at":"2026-06-05T01:15:05.430185+00:00"},{"alias_kind":"pith_short_12","alias_value":"F4IY44XGLIR2","created_at":"2026-06-05T01:15:05.430185+00:00"},{"alias_kind":"pith_short_16","alias_value":"F4IY44XGLIR2PX7L","created_at":"2026-06-05T01:15:05.430185+00:00"},{"alias_kind":"pith_short_8","alias_value":"F4IY44XG","created_at":"2026-06-05T01:15:05.430185+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD","json":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD.json","graph_json":"https://pith.science/api/pith-number/F4IY44XGLIR2PX7LRQ2BO3GMDD/graph.json","events_json":"https://pith.science/api/pith-number/F4IY44XGLIR2PX7LRQ2BO3GMDD/events.json","paper":"https://pith.science/paper/F4IY44XG"},"agent_actions":{"view_html":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD","download_json":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD.json","view_paper":"https://pith.science/paper/F4IY44XG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05844&json=true","fetch_graph":"https://pith.science/api/pith-number/F4IY44XGLIR2PX7LRQ2BO3GMDD/graph.json","fetch_events":"https://pith.science/api/pith-number/F4IY44XGLIR2PX7LRQ2BO3GMDD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD/action/storage_attestation","attest_author":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD/action/author_attestation","sign_citation":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD/action/citation_signature","submit_replication":"https://pith.science/pith/F4IY44XGLIR2PX7LRQ2BO3GMDD/action/replication_record"}},"created_at":"2026-06-05T01:15:05.430185+00:00","updated_at":"2026-06-05T01:15:05.430185+00:00"}