{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:H7JHMNN3Y4KLDELCKK7TKHR6I3","short_pith_number":"pith:H7JHMNN3","schema_version":"1.0","canonical_sha256":"3fd27635bbc714b1916252bf351e3e46e298313dfda900d325fc748ef4ec1a40","source":{"kind":"arxiv","id":"2510.13817","version":2},"attestation_state":"computed","paper":{"title":"What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.LG","authors_text":"Danny Yuxing Huang, Rameen Mahmood, Sai Teja Peddinti, Tousif Ahmed","submitted_at":"2025-09-24T05:33:48Z","abstract_excerpt":"The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability. This challenge is especially pronounced in open-world environments, where network traffic metadata is often sparse, noisy, or adversarial. To address this problem, we introduce a semantic inference pipeline that reframes device identification as a language modeling task over real-world network metadata. As this approach depends on reliable supervision, we first construct high-fidelity vendor labels for the IoT Inspector dataset, the largest rea"},"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":"2510.13817","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-24T05:33:48Z","cross_cats_sorted":["cs.NI"],"title_canon_sha256":"7c5ad1e2fc015c2aaeb111085e450c4904bb3626b6bdeec1086226e15e86d9ab","abstract_canon_sha256":"61329d9a87a4132cbb9846ade037f15b286700a340f189acf59a10a192053027"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T01:20:34.894483Z","signature_b64":"ChL1E8eIdi4JKFh63bRiy3l/Ure134sEwov9wYIpDHfJXXSUlvo78m367peoDc2bSnF1vweTjmng0LjQLpf7AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fd27635bbc714b1916252bf351e3e46e298313dfda900d325fc748ef4ec1a40","last_reissued_at":"2026-07-09T01:20:34.893968Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T01:20:34.893968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.LG","authors_text":"Danny Yuxing Huang, Rameen Mahmood, Sai Teja Peddinti, Tousif Ahmed","submitted_at":"2025-09-24T05:33:48Z","abstract_excerpt":"The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability. This challenge is especially pronounced in open-world environments, where network traffic metadata is often sparse, noisy, or adversarial. To address this problem, we introduce a semantic inference pipeline that reframes device identification as a language modeling task over real-world network metadata. As this approach depends on reliable supervision, we first construct high-fidelity vendor labels for the IoT Inspector dataset, the largest rea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.13817","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/2510.13817/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":"2510.13817","created_at":"2026-07-09T01:20:34.894031+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.13817v2","created_at":"2026-07-09T01:20:34.894031+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.13817","created_at":"2026-07-09T01:20:34.894031+00:00"},{"alias_kind":"pith_short_12","alias_value":"H7JHMNN3Y4KL","created_at":"2026-07-09T01:20:34.894031+00:00"},{"alias_kind":"pith_short_16","alias_value":"H7JHMNN3Y4KLDELC","created_at":"2026-07-09T01:20:34.894031+00:00"},{"alias_kind":"pith_short_8","alias_value":"H7JHMNN3","created_at":"2026-07-09T01:20:34.894031+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.01616","citing_title":"Learning Behavioral Signals from Encrypted Smartphone Network Traffic","ref_index":41,"is_internal_anchor":true},{"citing_arxiv_id":"2605.01616","citing_title":"Learning Behavioral Signals from Encrypted Smartphone Network Traffic","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3","json":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3.json","graph_json":"https://pith.science/api/pith-number/H7JHMNN3Y4KLDELCKK7TKHR6I3/graph.json","events_json":"https://pith.science/api/pith-number/H7JHMNN3Y4KLDELCKK7TKHR6I3/events.json","paper":"https://pith.science/paper/H7JHMNN3"},"agent_actions":{"view_html":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3","download_json":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3.json","view_paper":"https://pith.science/paper/H7JHMNN3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.13817&json=true","fetch_graph":"https://pith.science/api/pith-number/H7JHMNN3Y4KLDELCKK7TKHR6I3/graph.json","fetch_events":"https://pith.science/api/pith-number/H7JHMNN3Y4KLDELCKK7TKHR6I3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3/action/storage_attestation","attest_author":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3/action/author_attestation","sign_citation":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3/action/citation_signature","submit_replication":"https://pith.science/pith/H7JHMNN3Y4KLDELCKK7TKHR6I3/action/replication_record"}},"created_at":"2026-07-09T01:20:34.894031+00:00","updated_at":"2026-07-09T01:20:34.894031+00:00"}