{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:H22DNXETQZSFJWUFOO5TIQIIQN","short_pith_number":"pith:H22DNXET","schema_version":"1.0","canonical_sha256":"3eb436dc93866454da8573bb34410883673f057a3054a4b5fb564e3388f3d142","source":{"kind":"arxiv","id":"2607.17035","version":1},"attestation_state":"computed","paper":{"title":"Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Amal Alshehri, Cihan Tunc","submitted_at":"2026-07-19T02:35:24Z","abstract_excerpt":"While the Internet of Things (IoT) has become essential, they introduced serious security and privacy challenges, especially for mission-critical environments. Legacy devices are vulnerable to viruses, data breaches, and unauthorized access, and updating these devices would be infeasibly costly. As a solution, this paper presents a Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture, with FL for anomaly detection integrating privacy-preserving distributed learning, continuous identity verification, and LLM-driven autonomous threat response into a unified pipel"},"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":"2607.17035","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-07-19T02:35:24Z","cross_cats_sorted":[],"title_canon_sha256":"66180e9fe8fab87e44701f4403994a50eb6cafb4e37ff27c5315ba584a13ece1","abstract_canon_sha256":"c933035084fd1fbb450216684678ecdbd96b4df96c80ab784777b071b7b628e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:11.612994Z","signature_b64":"tR08oF3kSgU86ajfDhSTs3XVeoSyJ9Z5WdINTQDs6h8LpyuW6LezqtFWPN4ZnNOSm35zWvhPTG2c8GNwnP27BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3eb436dc93866454da8573bb34410883673f057a3054a4b5fb564e3388f3d142","last_reissued_at":"2026-07-21T01:21:11.612211Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:11.612211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Amal Alshehri, Cihan Tunc","submitted_at":"2026-07-19T02:35:24Z","abstract_excerpt":"While the Internet of Things (IoT) has become essential, they introduced serious security and privacy challenges, especially for mission-critical environments. Legacy devices are vulnerable to viruses, data breaches, and unauthorized access, and updating these devices would be infeasibly costly. As a solution, this paper presents a Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture, with FL for anomaly detection integrating privacy-preserving distributed learning, continuous identity verification, and LLM-driven autonomous threat response into a unified pipel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17035","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/2607.17035/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":"2607.17035","created_at":"2026-07-21T01:21:11.612618+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17035v1","created_at":"2026-07-21T01:21:11.612618+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17035","created_at":"2026-07-21T01:21:11.612618+00:00"},{"alias_kind":"pith_short_12","alias_value":"H22DNXETQZSF","created_at":"2026-07-21T01:21:11.612618+00:00"},{"alias_kind":"pith_short_16","alias_value":"H22DNXETQZSFJWUF","created_at":"2026-07-21T01:21:11.612618+00:00"},{"alias_kind":"pith_short_8","alias_value":"H22DNXET","created_at":"2026-07-21T01:21:11.612618+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/H22DNXETQZSFJWUFOO5TIQIIQN","json":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN.json","graph_json":"https://pith.science/api/pith-number/H22DNXETQZSFJWUFOO5TIQIIQN/graph.json","events_json":"https://pith.science/api/pith-number/H22DNXETQZSFJWUFOO5TIQIIQN/events.json","paper":"https://pith.science/paper/H22DNXET"},"agent_actions":{"view_html":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN","download_json":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN.json","view_paper":"https://pith.science/paper/H22DNXET","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17035&json=true","fetch_graph":"https://pith.science/api/pith-number/H22DNXETQZSFJWUFOO5TIQIIQN/graph.json","fetch_events":"https://pith.science/api/pith-number/H22DNXETQZSFJWUFOO5TIQIIQN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN/action/storage_attestation","attest_author":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN/action/author_attestation","sign_citation":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN/action/citation_signature","submit_replication":"https://pith.science/pith/H22DNXETQZSFJWUFOO5TIQIIQN/action/replication_record"}},"created_at":"2026-07-21T01:21:11.612618+00:00","updated_at":"2026-07-21T01:21:11.612618+00:00"}