{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:F72NUVHW6PF6G6BI3MSQ2XVIYZ","short_pith_number":"pith:F72NUVHW","schema_version":"1.0","canonical_sha256":"2ff4da54f6f3cbe37828db250d5ea8c645fd0b777de0421303209b156a1da006","source":{"kind":"arxiv","id":"2501.00790","version":2},"attestation_state":"computed","paper":{"title":"LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.ET"],"primary_cat":"cs.CR","authors_text":"Muhammet Anil Yagiz, Polat Goktas","submitted_at":"2025-01-01T10:00:49Z","abstract_excerpt":"The rapid proliferation of Industrial Internet of Things (IIoT) systems necessitates advanced, interpretable, and scalable intrusion detection systems (IDS) to combat emerging cyber threats. Traditional IDS face challenges such as high computational demands, limited explainability, and inflexibility against evolving attack patterns. To address these limitations, this study introduces the Lightweight Explainable Network Security framework (LENS-XAI), which combines robust intrusion detection with enhanced interpretability and scalability. LENS-XAI integrates knowledge distillation, variational "},"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":"2501.00790","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-01-01T10:00:49Z","cross_cats_sorted":["cs.AI","cs.CY","cs.ET"],"title_canon_sha256":"e640a7cc06a17ec4ddd2db0e9598d52de346facf6165c56138e37e71b9f5361e","abstract_canon_sha256":"b4c9a43f3709df2aca3bd5dd0e41105473ffdeddc54e817ddbc8efd12c3ae598"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:27.471880Z","signature_b64":"Kw6FaMv7lqQnSTjGYQpJ7r5XVfbRT10UQ93VeOy9vKNSc22aqZ3IJKaNjfO85PA7GeEkq9T8AzhQZTbTMIa5DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ff4da54f6f3cbe37828db250d5ea8c645fd0b777de0421303209b156a1da006","last_reissued_at":"2026-07-05T09:58:27.471438Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:27.471438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.ET"],"primary_cat":"cs.CR","authors_text":"Muhammet Anil Yagiz, Polat Goktas","submitted_at":"2025-01-01T10:00:49Z","abstract_excerpt":"The rapid proliferation of Industrial Internet of Things (IIoT) systems necessitates advanced, interpretable, and scalable intrusion detection systems (IDS) to combat emerging cyber threats. Traditional IDS face challenges such as high computational demands, limited explainability, and inflexibility against evolving attack patterns. To address these limitations, this study introduces the Lightweight Explainable Network Security framework (LENS-XAI), which combines robust intrusion detection with enhanced interpretability and scalability. LENS-XAI integrates knowledge distillation, variational "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.00790","kind":"arxiv","version":2},"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/2501.00790/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":"2501.00790","created_at":"2026-07-05T09:58:27.471494+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.00790v2","created_at":"2026-07-05T09:58:27.471494+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.00790","created_at":"2026-07-05T09:58:27.471494+00:00"},{"alias_kind":"pith_short_12","alias_value":"F72NUVHW6PF6","created_at":"2026-07-05T09:58:27.471494+00:00"},{"alias_kind":"pith_short_16","alias_value":"F72NUVHW6PF6G6BI","created_at":"2026-07-05T09:58:27.471494+00:00"},{"alias_kind":"pith_short_8","alias_value":"F72NUVHW","created_at":"2026-07-05T09:58:27.471494+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.11618","citing_title":"Enhancing IoT Network Security through Adaptive Curriculum Learning and XAI","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ","json":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ.json","graph_json":"https://pith.science/api/pith-number/F72NUVHW6PF6G6BI3MSQ2XVIYZ/graph.json","events_json":"https://pith.science/api/pith-number/F72NUVHW6PF6G6BI3MSQ2XVIYZ/events.json","paper":"https://pith.science/paper/F72NUVHW"},"agent_actions":{"view_html":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ","download_json":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ.json","view_paper":"https://pith.science/paper/F72NUVHW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.00790&json=true","fetch_graph":"https://pith.science/api/pith-number/F72NUVHW6PF6G6BI3MSQ2XVIYZ/graph.json","fetch_events":"https://pith.science/api/pith-number/F72NUVHW6PF6G6BI3MSQ2XVIYZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ/action/storage_attestation","attest_author":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ/action/author_attestation","sign_citation":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ/action/citation_signature","submit_replication":"https://pith.science/pith/F72NUVHW6PF6G6BI3MSQ2XVIYZ/action/replication_record"}},"created_at":"2026-07-05T09:58:27.471494+00:00","updated_at":"2026-07-05T09:58:27.471494+00:00"}