{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BNDFUENB2QXG5T62XM3CNSVGJM","short_pith_number":"pith:BNDFUENB","schema_version":"1.0","canonical_sha256":"0b465a11a1d42e6ecfdabb3626caa64b2212580bc3fa2ba849f5f6a252f2dd1c","source":{"kind":"arxiv","id":"2509.00069","version":1},"attestation_state":"computed","paper":{"title":"AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dumindu Kankanamge, Ekaterina Gilman, Mourad Oussalah, Prasasthy Balasubramanian","submitted_at":"2025-08-26T12:45:13Z","abstract_excerpt":"Conversational AI and Large Language Models (LLMs) have become powerful tools across domains, including cybersecurity, where they help detect threats early and improve response times. However, challenges such as false positives and complex model management still limit trust. Although Explainable AI (XAI) aims to make AI decisions more transparent, many security analysts remain uncertain about its usefulness. This study presents a framework that detects anomalies and provides high-quality explanations through visual tools BERTViz and Captum, combined with natural language reports based on atten"},"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":"2509.00069","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T12:45:13Z","cross_cats_sorted":[],"title_canon_sha256":"6fab77182afc6229d90a653fdc078228a8cdfce76c02eeed77bbbbd9acbb306a","abstract_canon_sha256":"70bc1b3ee3eacf483a44628fc9af35777cf6c960eef802ce32affa48dbb69973"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:08.959061Z","signature_b64":"ERhvJsyxkaiE/0DbtGFATAHXtvG5GXG6fAoJfhb05XnwBFbeQhgxOBL6tdEkl5z2hAvRAg0+bFUbu2ikvoscCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b465a11a1d42e6ecfdabb3626caa64b2212580bc3fa2ba849f5f6a252f2dd1c","last_reissued_at":"2026-07-05T12:02:08.958558Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:08.958558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dumindu Kankanamge, Ekaterina Gilman, Mourad Oussalah, Prasasthy Balasubramanian","submitted_at":"2025-08-26T12:45:13Z","abstract_excerpt":"Conversational AI and Large Language Models (LLMs) have become powerful tools across domains, including cybersecurity, where they help detect threats early and improve response times. However, challenges such as false positives and complex model management still limit trust. Although Explainable AI (XAI) aims to make AI decisions more transparent, many security analysts remain uncertain about its usefulness. This study presents a framework that detects anomalies and provides high-quality explanations through visual tools BERTViz and Captum, combined with natural language reports based on atten"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.00069","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/2509.00069/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":"2509.00069","created_at":"2026-07-05T12:02:08.958623+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.00069v1","created_at":"2026-07-05T12:02:08.958623+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.00069","created_at":"2026-07-05T12:02:08.958623+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNDFUENB2QXG","created_at":"2026-07-05T12:02:08.958623+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNDFUENB2QXG5T62","created_at":"2026-07-05T12:02:08.958623+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNDFUENB","created_at":"2026-07-05T12:02:08.958623+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/BNDFUENB2QXG5T62XM3CNSVGJM","json":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM.json","graph_json":"https://pith.science/api/pith-number/BNDFUENB2QXG5T62XM3CNSVGJM/graph.json","events_json":"https://pith.science/api/pith-number/BNDFUENB2QXG5T62XM3CNSVGJM/events.json","paper":"https://pith.science/paper/BNDFUENB"},"agent_actions":{"view_html":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM","download_json":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM.json","view_paper":"https://pith.science/paper/BNDFUENB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.00069&json=true","fetch_graph":"https://pith.science/api/pith-number/BNDFUENB2QXG5T62XM3CNSVGJM/graph.json","fetch_events":"https://pith.science/api/pith-number/BNDFUENB2QXG5T62XM3CNSVGJM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM/action/storage_attestation","attest_author":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM/action/author_attestation","sign_citation":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM/action/citation_signature","submit_replication":"https://pith.science/pith/BNDFUENB2QXG5T62XM3CNSVGJM/action/replication_record"}},"created_at":"2026-07-05T12:02:08.958623+00:00","updated_at":"2026-07-05T12:02:08.958623+00:00"}