{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2JVDAS7LUMCZIWZXAVDAE64SX2","short_pith_number":"pith:2JVDAS7L","schema_version":"1.0","canonical_sha256":"d26a304beba305945b370546027b92be9c33857a46345f11c326d554bb457b97","source":{"kind":"arxiv","id":"2606.17845","version":1},"attestation_state":"computed","paper":{"title":"UAV-CAS: A Calibrated Digital-Twin Dataset for Intrusion Detection in UAV Swarm Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Bharat Bhargava, Shafkat Islam, Sripath Mishra, Zizheng Liu","submitted_at":"2026-06-16T12:13:48Z","abstract_excerpt":"Intrusion detection systems (IDS) trained on wired-network benchmarks degrade sharply in real-world unmanned aerial vehicle (UAV) swarms, where mobility, fluctuating link quality, and decentralized routing reshape traffic distributions. Existing UAV-specific datasets also do not systematically vary these conditions, leaving no way to train or test an IDS against the very shift that defeats it. We present UAV-CAS, a large-scale labeled flow dataset for UAV-network intrusion detection, generated by a Containernet digital twin that is systematically calibrated against AERPAW testbed measurements."},"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":"2606.17845","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.NI","submitted_at":"2026-06-16T12:13:48Z","cross_cats_sorted":[],"title_canon_sha256":"04c3bb6b68f5665abb48aec180dc7c4d12a90e8bfcd355f19f87e757aa53c424","abstract_canon_sha256":"ba847e4a4a8a2291482d713205dab31257163cceea774715aa41009981ba007d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:41.280288Z","signature_b64":"CqFakykK8W297l5x7aZwRBROQwGMEYpJH4XlqD/i/r+88OMvbfqLa119oY/qDgePDscbt74knMm+iVoZgKpqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d26a304beba305945b370546027b92be9c33857a46345f11c326d554bb457b97","last_reissued_at":"2026-06-19T16:10:41.279842Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:41.279842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UAV-CAS: A Calibrated Digital-Twin Dataset for Intrusion Detection in UAV Swarm Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Bharat Bhargava, Shafkat Islam, Sripath Mishra, Zizheng Liu","submitted_at":"2026-06-16T12:13:48Z","abstract_excerpt":"Intrusion detection systems (IDS) trained on wired-network benchmarks degrade sharply in real-world unmanned aerial vehicle (UAV) swarms, where mobility, fluctuating link quality, and decentralized routing reshape traffic distributions. Existing UAV-specific datasets also do not systematically vary these conditions, leaving no way to train or test an IDS against the very shift that defeats it. We present UAV-CAS, a large-scale labeled flow dataset for UAV-network intrusion detection, generated by a Containernet digital twin that is systematically calibrated against AERPAW testbed measurements."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.17845","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/2606.17845/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":"2606.17845","created_at":"2026-06-19T16:10:41.279914+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.17845v1","created_at":"2026-06-19T16:10:41.279914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.17845","created_at":"2026-06-19T16:10:41.279914+00:00"},{"alias_kind":"pith_short_12","alias_value":"2JVDAS7LUMCZ","created_at":"2026-06-19T16:10:41.279914+00:00"},{"alias_kind":"pith_short_16","alias_value":"2JVDAS7LUMCZIWZX","created_at":"2026-06-19T16:10:41.279914+00:00"},{"alias_kind":"pith_short_8","alias_value":"2JVDAS7L","created_at":"2026-06-19T16:10:41.279914+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/2JVDAS7LUMCZIWZXAVDAE64SX2","json":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2.json","graph_json":"https://pith.science/api/pith-number/2JVDAS7LUMCZIWZXAVDAE64SX2/graph.json","events_json":"https://pith.science/api/pith-number/2JVDAS7LUMCZIWZXAVDAE64SX2/events.json","paper":"https://pith.science/paper/2JVDAS7L"},"agent_actions":{"view_html":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2","download_json":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2.json","view_paper":"https://pith.science/paper/2JVDAS7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.17845&json=true","fetch_graph":"https://pith.science/api/pith-number/2JVDAS7LUMCZIWZXAVDAE64SX2/graph.json","fetch_events":"https://pith.science/api/pith-number/2JVDAS7LUMCZIWZXAVDAE64SX2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2/action/storage_attestation","attest_author":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2/action/author_attestation","sign_citation":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2/action/citation_signature","submit_replication":"https://pith.science/pith/2JVDAS7LUMCZIWZXAVDAE64SX2/action/replication_record"}},"created_at":"2026-06-19T16:10:41.279914+00:00","updated_at":"2026-06-19T16:10:41.279914+00:00"}