{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JLASMLP3NTIP6N2HNRALCD5RI3","short_pith_number":"pith:JLASMLP3","schema_version":"1.0","canonical_sha256":"4ac1262dfb6cd0ff37476c40b10fb146f9ffe11fc9465b37074de8a117c35349","source":{"kind":"arxiv","id":"2208.08255","version":1},"attestation_state":"computed","paper":{"title":"On the Elements of Datasets for Cyber Physical Systems Security","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.CR","authors_text":"Ashraf Tantawy","submitted_at":"2022-08-17T12:20:57Z","abstract_excerpt":"Datasets are essential to apply AI algorithms to Cyber Physical System (CPS) Security. Due to scarcity of real CPS datasets, researchers elected to generate their own datasets using either real or virtualized testbeds. However, unlike other AI domains, a CPS is a complex system with many interfaces that determine its behavior. A dataset that comprises merely a collection of sensor measurements and network traffic may not be sufficient to develop resilient AI defensive or offensive agents. In this paper, we study the \\emph{elements} of CPS security datasets required to capture the system behavi"},"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":"2208.08255","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2022-08-17T12:20:57Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"e234a57a6bf4c8768473b595720ddc0a66410c54814e9c57ca816a723c5713b5","abstract_canon_sha256":"15054e2a777af2996eb555d4fa2a351f01516a75af77dc24f3810832537f5bff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:25.707559Z","signature_b64":"bIJVT40mNIxxhTd/sAPVLjhzBu3Rtk7iLX3UgCaling1DGe99K0x68q7lTi+etrG7k3nPmaOIq8tYcN2SFF0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ac1262dfb6cd0ff37476c40b10fb146f9ffe11fc9465b37074de8a117c35349","last_reissued_at":"2026-07-05T04:49:25.707139Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:25.707139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Elements of Datasets for Cyber Physical Systems Security","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.CR","authors_text":"Ashraf Tantawy","submitted_at":"2022-08-17T12:20:57Z","abstract_excerpt":"Datasets are essential to apply AI algorithms to Cyber Physical System (CPS) Security. Due to scarcity of real CPS datasets, researchers elected to generate their own datasets using either real or virtualized testbeds. However, unlike other AI domains, a CPS is a complex system with many interfaces that determine its behavior. A dataset that comprises merely a collection of sensor measurements and network traffic may not be sufficient to develop resilient AI defensive or offensive agents. In this paper, we study the \\emph{elements} of CPS security datasets required to capture the system behavi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.08255","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/2208.08255/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":"2208.08255","created_at":"2026-07-05T04:49:25.707197+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.08255v1","created_at":"2026-07-05T04:49:25.707197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.08255","created_at":"2026-07-05T04:49:25.707197+00:00"},{"alias_kind":"pith_short_12","alias_value":"JLASMLP3NTIP","created_at":"2026-07-05T04:49:25.707197+00:00"},{"alias_kind":"pith_short_16","alias_value":"JLASMLP3NTIP6N2H","created_at":"2026-07-05T04:49:25.707197+00:00"},{"alias_kind":"pith_short_8","alias_value":"JLASMLP3","created_at":"2026-07-05T04:49:25.707197+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.00929","citing_title":"PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3","json":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3.json","graph_json":"https://pith.science/api/pith-number/JLASMLP3NTIP6N2HNRALCD5RI3/graph.json","events_json":"https://pith.science/api/pith-number/JLASMLP3NTIP6N2HNRALCD5RI3/events.json","paper":"https://pith.science/paper/JLASMLP3"},"agent_actions":{"view_html":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3","download_json":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3.json","view_paper":"https://pith.science/paper/JLASMLP3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.08255&json=true","fetch_graph":"https://pith.science/api/pith-number/JLASMLP3NTIP6N2HNRALCD5RI3/graph.json","fetch_events":"https://pith.science/api/pith-number/JLASMLP3NTIP6N2HNRALCD5RI3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3/action/storage_attestation","attest_author":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3/action/author_attestation","sign_citation":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3/action/citation_signature","submit_replication":"https://pith.science/pith/JLASMLP3NTIP6N2HNRALCD5RI3/action/replication_record"}},"created_at":"2026-07-05T04:49:25.707197+00:00","updated_at":"2026-07-05T04:49:25.707197+00:00"}