{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YYAETXEPL2WAWZ32NROOLSFB4J","short_pith_number":"pith:YYAETXEP","schema_version":"1.0","canonical_sha256":"c60049dc8f5eac0b677a6c5ce5c8a1e276467de9dca179ead885a1b7b6d07ba9","source":{"kind":"arxiv","id":"2509.07392","version":1},"attestation_state":"computed","paper":{"title":"Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Gyuyeon Na, Hyemin Lee, HyeonJeong Cha, Jaeyoung Choi, Minjung Park, Sangmi Chai, Soyoun Kim, Sua Lee, Sunyoung Moon","submitted_at":"2025-09-09T05:14:26Z","abstract_excerpt":"Blockchain transaction networks are complex, with evolving temporal patterns and inter-node relationships. To detect illicit activities, we propose a hybrid GCN-GRU model that captures both structural and sequential features. Using real Bitcoin transaction data (2020-2024), our model achieved 0.9470 Accuracy and 0.9807 AUC-ROC, outperforming all baselines."},"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":"2509.07392","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-09T05:14:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e000e5f2db8bed48d59b3decf6dc1da07377522d2ee5ba742328ef602df233a8","abstract_canon_sha256":"026ab9392ec93fda77245addb5340cd220bfb1aeeb9592edb89446c7e402e815"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:07:20.814282Z","signature_b64":"KxNSwYpFf1S0WIt2GgBsyuUsoXlGCg2IRWZAd7cvJ3R8GDCeKx3Y7+D8dph2c70KFLoeVWNX+j946ppaKEZKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c60049dc8f5eac0b677a6c5ce5c8a1e276467de9dca179ead885a1b7b6d07ba9","last_reissued_at":"2026-07-05T12:07:20.813746Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:07:20.813746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Gyuyeon Na, Hyemin Lee, HyeonJeong Cha, Jaeyoung Choi, Minjung Park, Sangmi Chai, Soyoun Kim, Sua Lee, Sunyoung Moon","submitted_at":"2025-09-09T05:14:26Z","abstract_excerpt":"Blockchain transaction networks are complex, with evolving temporal patterns and inter-node relationships. To detect illicit activities, we propose a hybrid GCN-GRU model that captures both structural and sequential features. Using real Bitcoin transaction data (2020-2024), our model achieved 0.9470 Accuracy and 0.9807 AUC-ROC, outperforming all baselines."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.07392","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/2509.07392/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":"2509.07392","created_at":"2026-07-05T12:07:20.813799+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.07392v1","created_at":"2026-07-05T12:07:20.813799+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.07392","created_at":"2026-07-05T12:07:20.813799+00:00"},{"alias_kind":"pith_short_12","alias_value":"YYAETXEPL2WA","created_at":"2026-07-05T12:07:20.813799+00:00"},{"alias_kind":"pith_short_16","alias_value":"YYAETXEPL2WAWZ32","created_at":"2026-07-05T12:07:20.813799+00:00"},{"alias_kind":"pith_short_8","alias_value":"YYAETXEP","created_at":"2026-07-05T12:07:20.813799+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/YYAETXEPL2WAWZ32NROOLSFB4J","json":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J.json","graph_json":"https://pith.science/api/pith-number/YYAETXEPL2WAWZ32NROOLSFB4J/graph.json","events_json":"https://pith.science/api/pith-number/YYAETXEPL2WAWZ32NROOLSFB4J/events.json","paper":"https://pith.science/paper/YYAETXEP"},"agent_actions":{"view_html":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J","download_json":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J.json","view_paper":"https://pith.science/paper/YYAETXEP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.07392&json=true","fetch_graph":"https://pith.science/api/pith-number/YYAETXEPL2WAWZ32NROOLSFB4J/graph.json","fetch_events":"https://pith.science/api/pith-number/YYAETXEPL2WAWZ32NROOLSFB4J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J/action/storage_attestation","attest_author":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J/action/author_attestation","sign_citation":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J/action/citation_signature","submit_replication":"https://pith.science/pith/YYAETXEPL2WAWZ32NROOLSFB4J/action/replication_record"}},"created_at":"2026-07-05T12:07:20.813799+00:00","updated_at":"2026-07-05T12:07:20.813799+00:00"}