{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:TK63SCFB3I4EDORA6DQSFDMZJ4","short_pith_number":"pith:TK63SCFB","canonical_record":{"source":{"id":"1610.05945","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2016-10-19T10:06:14Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"ea25fdebd851de055ab2526e33f81b2acba340920f427ca82b6ae99d49c2bf2f","abstract_canon_sha256":"b471855d0ae9da4bda909d5148facf9e0c5bca0d11342501c26d86c85365363f"},"schema_version":"1.0"},"canonical_sha256":"9abdb908a1da3841ba20f0e1228d994f1321ca24aea58dfc495f73a9dec8df94","source":{"kind":"arxiv","id":"1610.05945","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.05945","created_at":"2026-05-18T01:01:50Z"},{"alias_kind":"arxiv_version","alias_value":"1610.05945v1","created_at":"2026-05-18T01:01:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.05945","created_at":"2026-05-18T01:01:50Z"},{"alias_kind":"pith_short_12","alias_value":"TK63SCFB3I4E","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"TK63SCFB3I4EDORA","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"TK63SCFB","created_at":"2026-05-18T12:30:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:TK63SCFB3I4EDORA6DQSFDMZJ4","target":"record","payload":{"canonical_record":{"source":{"id":"1610.05945","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2016-10-19T10:06:14Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"ea25fdebd851de055ab2526e33f81b2acba340920f427ca82b6ae99d49c2bf2f","abstract_canon_sha256":"b471855d0ae9da4bda909d5148facf9e0c5bca0d11342501c26d86c85365363f"},"schema_version":"1.0"},"canonical_sha256":"9abdb908a1da3841ba20f0e1228d994f1321ca24aea58dfc495f73a9dec8df94","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:01:50.603955Z","signature_b64":"oVBcqFe184jKoxFCZ6BbxBkYP1e8Qq1WCfguun7XBt/n7J1ih61VCIJ0Sb59tmlZaWGp8grQEiFcJk7Jz5o4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9abdb908a1da3841ba20f0e1228d994f1321ca24aea58dfc495f73a9dec8df94","last_reissued_at":"2026-05-18T01:01:50.603195Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:01:50.603195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.05945","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":"2026-05-18T01:01:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"njpILkR13yy741iFHIMZtneHs8IDF/sJwCI4fh+TtiZ+KwtRtT4wZctSjQZ6l0pq0N0KoXYFC9PrJuJV/AP8Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T09:13:38.787898Z"},"content_sha256":"f16ff31380a2eb22c644ad2b6200cf86a80cceeb3991219eb37561066e4331b7","schema_version":"1.0","event_id":"sha256:f16ff31380a2eb22c644ad2b6200cf86a80cceeb3991219eb37561066e4331b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:TK63SCFB3I4EDORA6DQSFDMZJ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A multi-task learning model for malware classification with useful file access pattern from API call sequence","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.SD","authors_text":"Siu Ming Yiu, Xin Wang","submitted_at":"2016-10-19T10:06:14Z","abstract_excerpt":"Based on API call sequences, semantic-aware and machine learning (ML) based malware classifiers can be built for malware detection or classification. Previous works concentrate on crafting and extracting various features from malware binaries, disassembled binaries or API calls via static or dynamic analysis and resorting to ML to build classifiers. However, they tend to involve too much feature engineering and fail to provide interpretability. We solve these two problems with the recent advances in deep learning: 1) RNN-based autoencoders (RNN-AEs) can automatically learn low-dimensional repr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.05945","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":"2026-05-18T01:01:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nd2hC2/MEXG9mHm5y3VW+Pn8C5AZNnSbYW0D55eiGS6RAY6ZbcK9P+/CD8hDQVbNwnUKWVl7wMtWfRrii+DgCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T09:13:38.788457Z"},"content_sha256":"1f594dae5d26cd7bbf9a6018c60e46700c88699eb48729cd7401bc0a78e8c411","schema_version":"1.0","event_id":"sha256:1f594dae5d26cd7bbf9a6018c60e46700c88699eb48729cd7401bc0a78e8c411"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TK63SCFB3I4EDORA6DQSFDMZJ4/bundle.json","state_url":"https://pith.science/pith/TK63SCFB3I4EDORA6DQSFDMZJ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TK63SCFB3I4EDORA6DQSFDMZJ4/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":"2026-06-07T09:13:38Z","links":{"resolver":"https://pith.science/pith/TK63SCFB3I4EDORA6DQSFDMZJ4","bundle":"https://pith.science/pith/TK63SCFB3I4EDORA6DQSFDMZJ4/bundle.json","state":"https://pith.science/pith/TK63SCFB3I4EDORA6DQSFDMZJ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TK63SCFB3I4EDORA6DQSFDMZJ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:TK63SCFB3I4EDORA6DQSFDMZJ4","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":"b471855d0ae9da4bda909d5148facf9e0c5bca0d11342501c26d86c85365363f","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2016-10-19T10:06:14Z","title_canon_sha256":"ea25fdebd851de055ab2526e33f81b2acba340920f427ca82b6ae99d49c2bf2f"},"schema_version":"1.0","source":{"id":"1610.05945","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.05945","created_at":"2026-05-18T01:01:50Z"},{"alias_kind":"arxiv_version","alias_value":"1610.05945v1","created_at":"2026-05-18T01:01:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.05945","created_at":"2026-05-18T01:01:50Z"},{"alias_kind":"pith_short_12","alias_value":"TK63SCFB3I4E","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"TK63SCFB3I4EDORA","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"TK63SCFB","created_at":"2026-05-18T12:30:44Z"}],"graph_snapshots":[{"event_id":"sha256:1f594dae5d26cd7bbf9a6018c60e46700c88699eb48729cd7401bc0a78e8c411","target":"graph","created_at":"2026-05-18T01:01:50Z","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":"Based on API call sequences, semantic-aware and machine learning (ML) based malware classifiers can be built for malware detection or classification. Previous works concentrate on crafting and extracting various features from malware binaries, disassembled binaries or API calls via static or dynamic analysis and resorting to ML to build classifiers. However, they tend to involve too much feature engineering and fail to provide interpretability. We solve these two problems with the recent advances in deep learning: 1) RNN-based autoencoders (RNN-AEs) can automatically learn low-dimensional repr","authors_text":"Siu Ming Yiu, Xin Wang","cross_cats":["cs.CR","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2016-10-19T10:06:14Z","title":"A multi-task learning model for malware classification with useful file access pattern from API call sequence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.05945","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:f16ff31380a2eb22c644ad2b6200cf86a80cceeb3991219eb37561066e4331b7","target":"record","created_at":"2026-05-18T01:01:50Z","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":"b471855d0ae9da4bda909d5148facf9e0c5bca0d11342501c26d86c85365363f","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2016-10-19T10:06:14Z","title_canon_sha256":"ea25fdebd851de055ab2526e33f81b2acba340920f427ca82b6ae99d49c2bf2f"},"schema_version":"1.0","source":{"id":"1610.05945","kind":"arxiv","version":1}},"canonical_sha256":"9abdb908a1da3841ba20f0e1228d994f1321ca24aea58dfc495f73a9dec8df94","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9abdb908a1da3841ba20f0e1228d994f1321ca24aea58dfc495f73a9dec8df94","first_computed_at":"2026-05-18T01:01:50.603195Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:01:50.603195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oVBcqFe184jKoxFCZ6BbxBkYP1e8Qq1WCfguun7XBt/n7J1ih61VCIJ0Sb59tmlZaWGp8grQEiFcJk7Jz5o4Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:01:50.603955Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.05945","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f16ff31380a2eb22c644ad2b6200cf86a80cceeb3991219eb37561066e4331b7","sha256:1f594dae5d26cd7bbf9a6018c60e46700c88699eb48729cd7401bc0a78e8c411"],"state_sha256":"871484cfd06df0feabb57f4f1bd592cb180eaff090bc8be8c5a483c2c71c1c15"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JJzMranPqjsqWhvE5nt5TzrPLb3A2qWyc9qFhkSzoMohOxtNId26bf6Lh7bHslcyDhtZNnO+cbJxGbEFIdI6BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T09:13:38.791463Z","bundle_sha256":"e407eb2680414a9a423e9586fc924768a2d8e44dce08c7e84fcad91c1756d2d4"}}