{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KYJWOTDIYUHP5WI3UVS7QIKBQV","short_pith_number":"pith:KYJWOTDI","schema_version":"1.0","canonical_sha256":"5613674c68c50efed91ba565f82141857564f3b9e8ae808df736905c678dda39","source":{"kind":"arxiv","id":"2412.19830","version":1},"attestation_state":"computed","paper":{"title":"A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Athar Sheikh, Daniel Adu Worae, Spyridon Mastorakis","submitted_at":"2024-12-19T22:38:41Z","abstract_excerpt":"The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large language models (LLMs) for IoT administrative tasks with a fine-tuned anomaly detection module for network traffic analysis. The framework streamlines administrative processes such as device management, troubleshooting, and security enforcement by harnessing contextual knowledge from IoT manuals and operational data. The anomaly detection model achieves state-of-th"},"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":"2412.19830","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-12-19T22:38:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"82a4dc6badb8714de37a8f63c40bb6b1e9d67e894390a335432a1e74c057e0c5","abstract_canon_sha256":"32e656cc7db5b9275073972036cc9a37e1fcce8b006b8b1f2d4cba4182e8916f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:50.112121Z","signature_b64":"6xpFL/UzjV3RLpxBdcoopG/rvGr9ALGhYbQUDy/CRioE95gaGGwDO8qCIC1V/2SHQQ4JB9P1W8zQGDN73n27BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5613674c68c50efed91ba565f82141857564f3b9e8ae808df736905c678dda39","last_reissued_at":"2026-07-05T09:54:50.111685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:50.111685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Athar Sheikh, Daniel Adu Worae, Spyridon Mastorakis","submitted_at":"2024-12-19T22:38:41Z","abstract_excerpt":"The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large language models (LLMs) for IoT administrative tasks with a fine-tuned anomaly detection module for network traffic analysis. The framework streamlines administrative processes such as device management, troubleshooting, and security enforcement by harnessing contextual knowledge from IoT manuals and operational data. The anomaly detection model achieves state-of-th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19830","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/2412.19830/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":"2412.19830","created_at":"2026-07-05T09:54:50.111742+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19830v1","created_at":"2026-07-05T09:54:50.111742+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19830","created_at":"2026-07-05T09:54:50.111742+00:00"},{"alias_kind":"pith_short_12","alias_value":"KYJWOTDIYUHP","created_at":"2026-07-05T09:54:50.111742+00:00"},{"alias_kind":"pith_short_16","alias_value":"KYJWOTDIYUHP5WI3","created_at":"2026-07-05T09:54:50.111742+00:00"},{"alias_kind":"pith_short_8","alias_value":"KYJWOTDI","created_at":"2026-07-05T09:54:50.111742+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.00240","citing_title":"LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV","json":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV.json","graph_json":"https://pith.science/api/pith-number/KYJWOTDIYUHP5WI3UVS7QIKBQV/graph.json","events_json":"https://pith.science/api/pith-number/KYJWOTDIYUHP5WI3UVS7QIKBQV/events.json","paper":"https://pith.science/paper/KYJWOTDI"},"agent_actions":{"view_html":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV","download_json":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV.json","view_paper":"https://pith.science/paper/KYJWOTDI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19830&json=true","fetch_graph":"https://pith.science/api/pith-number/KYJWOTDIYUHP5WI3UVS7QIKBQV/graph.json","fetch_events":"https://pith.science/api/pith-number/KYJWOTDIYUHP5WI3UVS7QIKBQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV/action/storage_attestation","attest_author":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV/action/author_attestation","sign_citation":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV/action/citation_signature","submit_replication":"https://pith.science/pith/KYJWOTDIYUHP5WI3UVS7QIKBQV/action/replication_record"}},"created_at":"2026-07-05T09:54:50.111742+00:00","updated_at":"2026-07-05T09:54:50.111742+00:00"}