{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DUKQCQQLBCERS5SZ7DDSN2BZGI","short_pith_number":"pith:DUKQCQQL","schema_version":"1.0","canonical_sha256":"1d1501420b0889197659f8c726e839320a442377963115f52e66dbeaf3522496","source":{"kind":"arxiv","id":"1608.05493","version":1},"attestation_state":"computed","paper":{"title":"Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.NI","authors_text":"Hiroyuki Kasai, Martin Kleinsteuber, Wolfgang Kellerer","submitted_at":"2016-08-19T05:06:58Z","abstract_excerpt":"This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows. Since traffic data is large-scale time-structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Cande"},"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":"1608.05493","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2016-08-19T05:06:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3e5dcfac15610591d24ecbbdd5c89fd93eecb5bfc0f7c104b233f8d0cb6dacfa","abstract_canon_sha256":"cb5ff137c92223c5356711514a066c583c07aa7a2c67b41cfd06ff25dcd37ce8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:50.876279Z","signature_b64":"Yprrq3tMsHDoybz/sH382tjB5rWLl7AexwsMCTRSqvEoUTx/VprfJuBHnL41cYYaB6frxDUu8oXIO37h5ZQBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d1501420b0889197659f8c726e839320a442377963115f52e66dbeaf3522496","last_reissued_at":"2026-05-18T00:12:50.875786Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:50.875786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.NI","authors_text":"Hiroyuki Kasai, Martin Kleinsteuber, Wolfgang Kellerer","submitted_at":"2016-08-19T05:06:58Z","abstract_excerpt":"This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows. Since traffic data is large-scale time-structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Cande"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05493","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":""},"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":"1608.05493","created_at":"2026-05-18T00:12:50.875858+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.05493v1","created_at":"2026-05-18T00:12:50.875858+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.05493","created_at":"2026-05-18T00:12:50.875858+00:00"},{"alias_kind":"pith_short_12","alias_value":"DUKQCQQLBCER","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DUKQCQQLBCERS5SZ","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DUKQCQQL","created_at":"2026-05-18T12:30:12.583610+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/DUKQCQQLBCERS5SZ7DDSN2BZGI","json":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI.json","graph_json":"https://pith.science/api/pith-number/DUKQCQQLBCERS5SZ7DDSN2BZGI/graph.json","events_json":"https://pith.science/api/pith-number/DUKQCQQLBCERS5SZ7DDSN2BZGI/events.json","paper":"https://pith.science/paper/DUKQCQQL"},"agent_actions":{"view_html":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI","download_json":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI.json","view_paper":"https://pith.science/paper/DUKQCQQL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.05493&json=true","fetch_graph":"https://pith.science/api/pith-number/DUKQCQQLBCERS5SZ7DDSN2BZGI/graph.json","fetch_events":"https://pith.science/api/pith-number/DUKQCQQLBCERS5SZ7DDSN2BZGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI/action/storage_attestation","attest_author":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI/action/author_attestation","sign_citation":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI/action/citation_signature","submit_replication":"https://pith.science/pith/DUKQCQQLBCERS5SZ7DDSN2BZGI/action/replication_record"}},"created_at":"2026-05-18T00:12:50.875858+00:00","updated_at":"2026-05-18T00:12:50.875858+00:00"}