{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:UC4T4FDWED536FEANKBIBHIYRM","short_pith_number":"pith:UC4T4FDW","canonical_record":{"source":{"id":"1805.01554","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-05-03T21:53:09Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"9b69609cb4b8f80a757ac165c689d280c634d5c757fdc32ec2209598f26576ac","abstract_canon_sha256":"49cdce10894bbbed34b51cbfc881a4d909e46dda62779ca8505db92d3cd5bedb"},"schema_version":"1.0"},"canonical_sha256":"a0b93e147620fbbf14806a82809d188b30dba04a5a08018100eee0a5e4bb4ed6","source":{"kind":"arxiv","id":"1805.01554","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.01554","created_at":"2026-05-18T00:16:47Z"},{"alias_kind":"arxiv_version","alias_value":"1805.01554v1","created_at":"2026-05-18T00:16:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01554","created_at":"2026-05-18T00:16:47Z"},{"alias_kind":"pith_short_12","alias_value":"UC4T4FDWED53","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UC4T4FDWED536FEA","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UC4T4FDW","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:UC4T4FDWED536FEANKBIBHIYRM","target":"record","payload":{"canonical_record":{"source":{"id":"1805.01554","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-05-03T21:53:09Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"9b69609cb4b8f80a757ac165c689d280c634d5c757fdc32ec2209598f26576ac","abstract_canon_sha256":"49cdce10894bbbed34b51cbfc881a4d909e46dda62779ca8505db92d3cd5bedb"},"schema_version":"1.0"},"canonical_sha256":"a0b93e147620fbbf14806a82809d188b30dba04a5a08018100eee0a5e4bb4ed6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:47.487738Z","signature_b64":"l8FnCBjx14jvysaYg9aLXSoAyqqY71sG0fcFgoyjys4HPxLwi+ZIiIXrzGcUa1SXR5jDsYN5xYwCoI9OW9xgDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0b93e147620fbbf14806a82809d188b30dba04a5a08018100eee0a5e4bb4ed6","last_reissued_at":"2026-05-18T00:16:47.487080Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:47.487080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.01554","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Si7EJeOmeeMxLH2XJjXtc9EYc7OSp9RCUFFU/uuAILv+wDmAXUDoYJ8yUMviVGM1OvdZRvPx7T79V6FfC+TfAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:24:58.623580Z"},"content_sha256":"29c86dcca4637b466218f8419c16755831bc1e2af051c7676c075471ed993616","schema_version":"1.0","event_id":"sha256:29c86dcca4637b466218f8419c16755831bc1e2af051c7676c075471ed993616"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:UC4T4FDWED536FEANKBIBHIYRM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CR","authors_text":"Minh Nguyen, Thien Huu Nguyen, Toan Nguyen","submitted_at":"2018-05-03T21:53:09Z","abstract_excerpt":"Anti-phishing aims to detect phishing content/documents in a pool of textual data. This is an important problem in cybersecurity that can help to guard users from fraudulent information. Natural language processing (NLP) offers a natural solution for this problem as it is capable of analyzing the textual content to perform intelligent recognition. In this work, we investigate state-of-the-art techniques for text categorization in NLP to address the problem of anti-phishing for emails (i.e, predicting if an email is phishing or not). These techniques are based on deep learning models that have "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01554","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JXZk2q617wDTTP0+mHUFTgDZCAu9zJRRVvE+gI5UoCSqvAVUo3XKARrBdiFCVjTCTICcHGfwJKsTlRBzy6BCAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:24:58.623925Z"},"content_sha256":"23f56d6ea9e82b7119c5c77778be4830f6d6c84802a8ad9140c5a15d70b6ae39","schema_version":"1.0","event_id":"sha256:23f56d6ea9e82b7119c5c77778be4830f6d6c84802a8ad9140c5a15d70b6ae39"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UC4T4FDWED536FEANKBIBHIYRM/bundle.json","state_url":"https://pith.science/pith/UC4T4FDWED536FEANKBIBHIYRM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UC4T4FDWED536FEANKBIBHIYRM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T14:24:58Z","links":{"resolver":"https://pith.science/pith/UC4T4FDWED536FEANKBIBHIYRM","bundle":"https://pith.science/pith/UC4T4FDWED536FEANKBIBHIYRM/bundle.json","state":"https://pith.science/pith/UC4T4FDWED536FEANKBIBHIYRM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UC4T4FDWED536FEANKBIBHIYRM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:UC4T4FDWED536FEANKBIBHIYRM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"49cdce10894bbbed34b51cbfc881a4d909e46dda62779ca8505db92d3cd5bedb","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-05-03T21:53:09Z","title_canon_sha256":"9b69609cb4b8f80a757ac165c689d280c634d5c757fdc32ec2209598f26576ac"},"schema_version":"1.0","source":{"id":"1805.01554","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.01554","created_at":"2026-05-18T00:16:47Z"},{"alias_kind":"arxiv_version","alias_value":"1805.01554v1","created_at":"2026-05-18T00:16:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01554","created_at":"2026-05-18T00:16:47Z"},{"alias_kind":"pith_short_12","alias_value":"UC4T4FDWED53","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UC4T4FDWED536FEA","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UC4T4FDW","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:23f56d6ea9e82b7119c5c77778be4830f6d6c84802a8ad9140c5a15d70b6ae39","target":"graph","created_at":"2026-05-18T00:16:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Anti-phishing aims to detect phishing content/documents in a pool of textual data. This is an important problem in cybersecurity that can help to guard users from fraudulent information. Natural language processing (NLP) offers a natural solution for this problem as it is capable of analyzing the textual content to perform intelligent recognition. In this work, we investigate state-of-the-art techniques for text categorization in NLP to address the problem of anti-phishing for emails (i.e, predicting if an email is phishing or not). These techniques are based on deep learning models that have ","authors_text":"Minh Nguyen, Thien Huu Nguyen, Toan Nguyen","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-05-03T21:53:09Z","title":"A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01554","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:29c86dcca4637b466218f8419c16755831bc1e2af051c7676c075471ed993616","target":"record","created_at":"2026-05-18T00:16:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"49cdce10894bbbed34b51cbfc881a4d909e46dda62779ca8505db92d3cd5bedb","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2018-05-03T21:53:09Z","title_canon_sha256":"9b69609cb4b8f80a757ac165c689d280c634d5c757fdc32ec2209598f26576ac"},"schema_version":"1.0","source":{"id":"1805.01554","kind":"arxiv","version":1}},"canonical_sha256":"a0b93e147620fbbf14806a82809d188b30dba04a5a08018100eee0a5e4bb4ed6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0b93e147620fbbf14806a82809d188b30dba04a5a08018100eee0a5e4bb4ed6","first_computed_at":"2026-05-18T00:16:47.487080Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:47.487080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"l8FnCBjx14jvysaYg9aLXSoAyqqY71sG0fcFgoyjys4HPxLwi+ZIiIXrzGcUa1SXR5jDsYN5xYwCoI9OW9xgDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:47.487738Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.01554","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29c86dcca4637b466218f8419c16755831bc1e2af051c7676c075471ed993616","sha256:23f56d6ea9e82b7119c5c77778be4830f6d6c84802a8ad9140c5a15d70b6ae39"],"state_sha256":"53ac43b0eb74157259a0944f89bc564f5a08db285d9cac1a2062af20c299e97c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NPMUhQFL+VFfVCPXk9ZSvK4vMuMlNrZH/1pL9hLA/3WaAZXZnVkDcLK/Zt9AELSlhrgCzRo0kZ6gnHwedrW3DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T14:24:58.626191Z","bundle_sha256":"963fc6c3d74253536e13da3f73bda0b11d89cd5a3cbac7a2140062d976746276"}}