{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:O7A74RB5O3DWPLSZM4WCDMTE3B","short_pith_number":"pith:O7A74RB5","schema_version":"1.0","canonical_sha256":"77c1fe443d76c767ae59672c21b264d85a0ad00394494606c8df85b46fd3da76","source":{"kind":"arxiv","id":"2405.17190","version":2},"attestation_state":"computed","paper":{"title":"SoK: Leveraging Transformers for Malware Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Elisa Bertino, Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar","submitted_at":"2024-05-27T14:14:07Z","abstract_excerpt":"The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application domain for transformers is cybersecurity, in particular the malware domain analysis. The reason is the flexibility of the transformer models in handling long sequential features and understanding contextual relationships. However, as the use of transformers for malware analysis is still in the infancy stage, it is critical to evaluate, systematize, and contextualize existing literature to foster future research. Thi"},"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":"2405.17190","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2024-05-27T14:14:07Z","cross_cats_sorted":[],"title_canon_sha256":"6a66d315faff8318122f6b4830570cee891ac2a9b00cb4984df705c24e2a40d9","abstract_canon_sha256":"4defcf922c9d9ed3eed3e9ed21095489d3e2902fedde24765a8ad191d4f24124"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:55:53.396581Z","signature_b64":"J93j8vpdZVJ73oNUmGyX8YigcpErYznFMSk/t2KexQH15BED4hOxtHa/cmxlD27o4HWrkSzQu4YVDV7j+FKBAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77c1fe443d76c767ae59672c21b264d85a0ad00394494606c8df85b46fd3da76","last_reissued_at":"2026-07-05T10:55:53.396109Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:55:53.396109Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SoK: Leveraging Transformers for Malware Analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Elisa Bertino, Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar","submitted_at":"2024-05-27T14:14:07Z","abstract_excerpt":"The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application domain for transformers is cybersecurity, in particular the malware domain analysis. The reason is the flexibility of the transformer models in handling long sequential features and understanding contextual relationships. However, as the use of transformers for malware analysis is still in the infancy stage, it is critical to evaluate, systematize, and contextualize existing literature to foster future research. Thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17190","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/2405.17190/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":"2405.17190","created_at":"2026-07-05T10:55:53.396165+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.17190v2","created_at":"2026-07-05T10:55:53.396165+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17190","created_at":"2026-07-05T10:55:53.396165+00:00"},{"alias_kind":"pith_short_12","alias_value":"O7A74RB5O3DW","created_at":"2026-07-05T10:55:53.396165+00:00"},{"alias_kind":"pith_short_16","alias_value":"O7A74RB5O3DWPLSZ","created_at":"2026-07-05T10:55:53.396165+00:00"},{"alias_kind":"pith_short_8","alias_value":"O7A74RB5","created_at":"2026-07-05T10:55:53.396165+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.12106","citing_title":"MalVis: A Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B","json":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B.json","graph_json":"https://pith.science/api/pith-number/O7A74RB5O3DWPLSZM4WCDMTE3B/graph.json","events_json":"https://pith.science/api/pith-number/O7A74RB5O3DWPLSZM4WCDMTE3B/events.json","paper":"https://pith.science/paper/O7A74RB5"},"agent_actions":{"view_html":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B","download_json":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B.json","view_paper":"https://pith.science/paper/O7A74RB5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.17190&json=true","fetch_graph":"https://pith.science/api/pith-number/O7A74RB5O3DWPLSZM4WCDMTE3B/graph.json","fetch_events":"https://pith.science/api/pith-number/O7A74RB5O3DWPLSZM4WCDMTE3B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B/action/storage_attestation","attest_author":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B/action/author_attestation","sign_citation":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B/action/citation_signature","submit_replication":"https://pith.science/pith/O7A74RB5O3DWPLSZM4WCDMTE3B/action/replication_record"}},"created_at":"2026-07-05T10:55:53.396165+00:00","updated_at":"2026-07-05T10:55:53.396165+00:00"}