{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6SO2AVVQPFBY44ASDGUA2RY2TY","short_pith_number":"pith:6SO2AVVQ","schema_version":"1.0","canonical_sha256":"f49da056b079438e701219a80d471a9e1e9c44535f4652e68b640d3c25c8aed1","source":{"kind":"arxiv","id":"1802.03358","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning for Malicious Flow Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aragorn Tseng, Tsungnan Lin, Yu-Jhe Li, Yun-Chun Chen","submitted_at":"2018-02-09T17:16:02Z","abstract_excerpt":"Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real data often encounters an issue of imbalanced data distribution which will lead to a gradient dilution issue. When training a neural network, this problem will not only result in a bias toward the majority class but show the inability to learn from the minority classes. In this paper, we propose an end-to-end trainable Tree-Shaped Deep Neural Network (TSDNN) "},"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":"1802.03358","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-09T17:16:02Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"cc01d573cf8f701add9ac0e13c61913efd81299fd6a1e82338e95b6d887246af","abstract_canon_sha256":"840dbb36b6f092b0ec72b21457414ce82b7143f707bfb1dd2b53c6ea3c75c2a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:57.837185Z","signature_b64":"IPihSndQOJJg/y02iLR/Wei3oAPekjGNBqLk7gf/EAjXcAsJaWCdWTFUXVZFPxY0nqBNcuhk6mZU/Hy87WOgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f49da056b079438e701219a80d471a9e1e9c44535f4652e68b640d3c25c8aed1","last_reissued_at":"2026-05-18T00:23:57.836548Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:57.836548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Malicious Flow Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aragorn Tseng, Tsungnan Lin, Yu-Jhe Li, Yun-Chun Chen","submitted_at":"2018-02-09T17:16:02Z","abstract_excerpt":"Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real data often encounters an issue of imbalanced data distribution which will lead to a gradient dilution issue. When training a neural network, this problem will not only result in a bias toward the majority class but show the inability to learn from the minority classes. In this paper, we propose an end-to-end trainable Tree-Shaped Deep Neural Network (TSDNN) "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03358","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1802.03358","created_at":"2026-05-18T00:23:57.836660+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.03358v1","created_at":"2026-05-18T00:23:57.836660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03358","created_at":"2026-05-18T00:23:57.836660+00:00"},{"alias_kind":"pith_short_12","alias_value":"6SO2AVVQPFBY","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"6SO2AVVQPFBY44AS","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"6SO2AVVQ","created_at":"2026-05-18T12:32:11.075285+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/6SO2AVVQPFBY44ASDGUA2RY2TY","json":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY.json","graph_json":"https://pith.science/api/pith-number/6SO2AVVQPFBY44ASDGUA2RY2TY/graph.json","events_json":"https://pith.science/api/pith-number/6SO2AVVQPFBY44ASDGUA2RY2TY/events.json","paper":"https://pith.science/paper/6SO2AVVQ"},"agent_actions":{"view_html":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY","download_json":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY.json","view_paper":"https://pith.science/paper/6SO2AVVQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.03358&json=true","fetch_graph":"https://pith.science/api/pith-number/6SO2AVVQPFBY44ASDGUA2RY2TY/graph.json","fetch_events":"https://pith.science/api/pith-number/6SO2AVVQPFBY44ASDGUA2RY2TY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY/action/storage_attestation","attest_author":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY/action/author_attestation","sign_citation":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY/action/citation_signature","submit_replication":"https://pith.science/pith/6SO2AVVQPFBY44ASDGUA2RY2TY/action/replication_record"}},"created_at":"2026-05-18T00:23:57.836660+00:00","updated_at":"2026-05-18T00:23:57.836660+00:00"}