{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RGR563VGALYU2KRCQMWPETLRGV","short_pith_number":"pith:RGR563VG","schema_version":"1.0","canonical_sha256":"89a3df6ea602f14d2a22832cf24d7135739f8a566c90bef528a4bac61dd1bcc5","source":{"kind":"arxiv","id":"2608.04047","version":1},"attestation_state":"computed","paper":{"title":"Beyond the QBER Threshold: A Temporal QBER Based Machine Learning Framework for Multi Attack Detection in BB84 QKD","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Amit Shukla, Deepak Singh, Devesh Kumar, Isha, Praful Hambarde, S.K Pal","submitted_at":"2026-08-04T08:04:16Z","abstract_excerpt":"Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping. However, stealthy attacks can remain below this threshold while still compromising channel security. This paper proposes a temporal QBER based machine learning framework for detecting and classifying eavesdropping attacks in BB84 QKD systems. Rather than relying on average session level QBER, the framework extracts 63 physics-informed temporal features capturing burst behavior, temporal instability, basis dependent asymmetry, and QBER loss interactions. "},"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":"2608.04047","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2026-08-04T08:04:16Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c2006707f911e8f96439627825339591db1fa45c6c557fff83cc6025ee63f126","abstract_canon_sha256":"015822a4064f39b5977f1d6c7731fcbf9a1d3ec94df326fb5d59803f13552759"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-06T01:28:04.272065Z","signature_b64":"7EiQ/3qIu47lhMJQtc37o4sEsMyNZlkku/dyZmifi1hlzT28C+C4VYqvpPPSn3YGzj34g/NpIuptfD7HjJjCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89a3df6ea602f14d2a22832cf24d7135739f8a566c90bef528a4bac61dd1bcc5","last_reissued_at":"2026-08-06T01:28:04.270481Z","signature_status":"signed_v1","first_computed_at":"2026-08-06T01:28:04.270481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond the QBER Threshold: A Temporal QBER Based Machine Learning Framework for Multi Attack Detection in BB84 QKD","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Amit Shukla, Deepak Singh, Devesh Kumar, Isha, Praful Hambarde, S.K Pal","submitted_at":"2026-08-04T08:04:16Z","abstract_excerpt":"Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping. However, stealthy attacks can remain below this threshold while still compromising channel security. This paper proposes a temporal QBER based machine learning framework for detecting and classifying eavesdropping attacks in BB84 QKD systems. Rather than relying on average session level QBER, the framework extracts 63 physics-informed temporal features capturing burst behavior, temporal instability, basis dependent asymmetry, and QBER loss interactions. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.04047","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/2608.04047/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":"2608.04047","created_at":"2026-08-06T01:28:04.272303+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.04047v1","created_at":"2026-08-06T01:28:04.272303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.04047","created_at":"2026-08-06T01:28:04.272303+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGR563VGALYU","created_at":"2026-08-06T01:28:04.272303+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGR563VGALYU2KRC","created_at":"2026-08-06T01:28:04.272303+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGR563VG","created_at":"2026-08-06T01:28:04.272303+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/RGR563VGALYU2KRCQMWPETLRGV","json":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV.json","graph_json":"https://pith.science/api/pith-number/RGR563VGALYU2KRCQMWPETLRGV/graph.json","events_json":"https://pith.science/api/pith-number/RGR563VGALYU2KRCQMWPETLRGV/events.json","paper":"https://pith.science/paper/RGR563VG"},"agent_actions":{"view_html":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV","download_json":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV.json","view_paper":"https://pith.science/paper/RGR563VG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.04047&json=true","fetch_graph":"https://pith.science/api/pith-number/RGR563VGALYU2KRCQMWPETLRGV/graph.json","fetch_events":"https://pith.science/api/pith-number/RGR563VGALYU2KRCQMWPETLRGV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV/action/storage_attestation","attest_author":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV/action/author_attestation","sign_citation":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV/action/citation_signature","submit_replication":"https://pith.science/pith/RGR563VGALYU2KRCQMWPETLRGV/action/replication_record"}},"created_at":"2026-08-06T01:28:04.272303+00:00","updated_at":"2026-08-06T01:28:04.272303+00:00"}