{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FWGMKSTBDSGDCEV7BYO5XTJLKL","short_pith_number":"pith:FWGMKSTB","schema_version":"1.0","canonical_sha256":"2d8cc54a611c8c3112bf0e1ddbcd2b52e074a622d04d82bd28dec60853a59b35","source":{"kind":"arxiv","id":"2502.09084","version":1},"attestation_state":"computed","paper":{"title":"Application of Tabular Transformer Architectures for Operating System Fingerprinting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NI"],"primary_cat":"cs.CR","authors_text":"Alejandro Pazos, Cristian R. Munteanu, Jose V\\'azquez-Naya, Rub\\'en P\\'erez-Jove","submitted_at":"2025-02-13T08:59:04Z","abstract_excerpt":"Operating System (OS) fingerprinting is essential for network management and cybersecurity, enabling accurate device identification based on network traffic analysis. Traditional rule-based tools such as Nmap and p0f face challenges in dynamic environments due to frequent OS updates and obfuscation techniques. While Machine Learning (ML) approaches have been explored, Deep Learning (DL) models, particularly Transformer architectures, remain unexploited in this domain. This study investigates the application of Tabular Transformer architectures-specifically TabTransformer and FT-Transformer-for"},"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":"2502.09084","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-02-13T08:59:04Z","cross_cats_sorted":["cs.LG","cs.NI"],"title_canon_sha256":"efe5df1025a8417f41c5c751f9699f1387a8066b2739372246c28eb4489eb598","abstract_canon_sha256":"74cd0350e0898cf717875dfc2bb85b180bf343102ba37f5771e783dc5f635ca7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:11.329722Z","signature_b64":"8FsAG+E3tgXgSBqgDwkzZS1ldlrjJ56R2OgaUgKORAb1MSV5bBErBHHJcKyc2UqDdE2qm1Zu+w/wxwXDYjt5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d8cc54a611c8c3112bf0e1ddbcd2b52e074a622d04d82bd28dec60853a59b35","last_reissued_at":"2026-06-11T01:09:11.328781Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:11.328781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Application of Tabular Transformer Architectures for Operating System Fingerprinting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.NI"],"primary_cat":"cs.CR","authors_text":"Alejandro Pazos, Cristian R. Munteanu, Jose V\\'azquez-Naya, Rub\\'en P\\'erez-Jove","submitted_at":"2025-02-13T08:59:04Z","abstract_excerpt":"Operating System (OS) fingerprinting is essential for network management and cybersecurity, enabling accurate device identification based on network traffic analysis. Traditional rule-based tools such as Nmap and p0f face challenges in dynamic environments due to frequent OS updates and obfuscation techniques. While Machine Learning (ML) approaches have been explored, Deep Learning (DL) models, particularly Transformer architectures, remain unexploited in this domain. This study investigates the application of Tabular Transformer architectures-specifically TabTransformer and FT-Transformer-for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09084","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/2502.09084/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":"2502.09084","created_at":"2026-06-11T01:09:11.328907+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.09084v1","created_at":"2026-06-11T01:09:11.328907+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09084","created_at":"2026-06-11T01:09:11.328907+00:00"},{"alias_kind":"pith_short_12","alias_value":"FWGMKSTBDSGD","created_at":"2026-06-11T01:09:11.328907+00:00"},{"alias_kind":"pith_short_16","alias_value":"FWGMKSTBDSGDCEV7","created_at":"2026-06-11T01:09:11.328907+00:00"},{"alias_kind":"pith_short_8","alias_value":"FWGMKSTB","created_at":"2026-06-11T01:09:11.328907+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2602.12825","citing_title":"Reliable Hierarchical Operating System Fingerprinting via Conformal Prediction","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL","json":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL.json","graph_json":"https://pith.science/api/pith-number/FWGMKSTBDSGDCEV7BYO5XTJLKL/graph.json","events_json":"https://pith.science/api/pith-number/FWGMKSTBDSGDCEV7BYO5XTJLKL/events.json","paper":"https://pith.science/paper/FWGMKSTB"},"agent_actions":{"view_html":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL","download_json":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL.json","view_paper":"https://pith.science/paper/FWGMKSTB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.09084&json=true","fetch_graph":"https://pith.science/api/pith-number/FWGMKSTBDSGDCEV7BYO5XTJLKL/graph.json","fetch_events":"https://pith.science/api/pith-number/FWGMKSTBDSGDCEV7BYO5XTJLKL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL/action/storage_attestation","attest_author":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL/action/author_attestation","sign_citation":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL/action/citation_signature","submit_replication":"https://pith.science/pith/FWGMKSTBDSGDCEV7BYO5XTJLKL/action/replication_record"}},"created_at":"2026-06-11T01:09:11.328907+00:00","updated_at":"2026-06-11T01:09:11.328907+00:00"}