{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:3X37PK43YE6WU4QI2LX4V56GQV","short_pith_number":"pith:3X37PK43","schema_version":"1.0","canonical_sha256":"ddf7f7ab9bc13d6a7208d2efcaf7c685750ded1ed1c12b7012665a9375c2d5d2","source":{"kind":"arxiv","id":"2606.30572","version":1},"attestation_state":"computed","paper":{"title":"A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Antonino Nocera, Anvin Mariya P. B., Asmitha K. A., Jithin S., Roshin Sleeba C., Serena Nicolazzo, Vinod P.","submitted_at":"2026-06-29T17:15:25Z","abstract_excerpt":"Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Traditional single-model detection mechanisms often fail to generalize across such diverse data, leading to degraded performance, particularly on obfuscated and rare malware samples. In this work, we propose a unified multi-task malware analysis framework based on Mixture of Experts (MoE) architectures. The proposed system evaluates performance across two different input representations, i.e., high-dimensional EMBER feature s"},"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":"2606.30572","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-06-29T17:15:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aa951c5356b6e1d684f9473a4938d9c4081ce097450d1aed98608ec1c6948a7d","abstract_canon_sha256":"b06ef766de921eaf746d99d70a5a1dac380e78e8082b09e37c14d182f0a33062"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:18:20.999261Z","signature_b64":"wpEzMu3VTxdgQSg+/SYU1urWsG0Wt/Erkk2qe2pVc9QMp6CyrNFIRiYFnv1aqlAtFmYEyWeLbc9eXJY0K8zNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ddf7f7ab9bc13d6a7208d2efcaf7c685750ded1ed1c12b7012665a9375c2d5d2","last_reissued_at":"2026-06-30T02:18:20.998698Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:18:20.998698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Antonino Nocera, Anvin Mariya P. B., Asmitha K. A., Jithin S., Roshin Sleeba C., Serena Nicolazzo, Vinod P.","submitted_at":"2026-06-29T17:15:25Z","abstract_excerpt":"Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Traditional single-model detection mechanisms often fail to generalize across such diverse data, leading to degraded performance, particularly on obfuscated and rare malware samples. In this work, we propose a unified multi-task malware analysis framework based on Mixture of Experts (MoE) architectures. The proposed system evaluates performance across two different input representations, i.e., high-dimensional EMBER feature s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30572","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/2606.30572/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":"2606.30572","created_at":"2026-06-30T02:18:20.998789+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30572v1","created_at":"2026-06-30T02:18:20.998789+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30572","created_at":"2026-06-30T02:18:20.998789+00:00"},{"alias_kind":"pith_short_12","alias_value":"3X37PK43YE6W","created_at":"2026-06-30T02:18:20.998789+00:00"},{"alias_kind":"pith_short_16","alias_value":"3X37PK43YE6WU4QI","created_at":"2026-06-30T02:18:20.998789+00:00"},{"alias_kind":"pith_short_8","alias_value":"3X37PK43","created_at":"2026-06-30T02:18:20.998789+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/3X37PK43YE6WU4QI2LX4V56GQV","json":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV.json","graph_json":"https://pith.science/api/pith-number/3X37PK43YE6WU4QI2LX4V56GQV/graph.json","events_json":"https://pith.science/api/pith-number/3X37PK43YE6WU4QI2LX4V56GQV/events.json","paper":"https://pith.science/paper/3X37PK43"},"agent_actions":{"view_html":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV","download_json":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV.json","view_paper":"https://pith.science/paper/3X37PK43","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30572&json=true","fetch_graph":"https://pith.science/api/pith-number/3X37PK43YE6WU4QI2LX4V56GQV/graph.json","fetch_events":"https://pith.science/api/pith-number/3X37PK43YE6WU4QI2LX4V56GQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV/action/storage_attestation","attest_author":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV/action/author_attestation","sign_citation":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV/action/citation_signature","submit_replication":"https://pith.science/pith/3X37PK43YE6WU4QI2LX4V56GQV/action/replication_record"}},"created_at":"2026-06-30T02:18:20.998789+00:00","updated_at":"2026-06-30T02:18:20.998789+00:00"}