{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LZ4IQVSE5XFNAPBOSI3T7CY2QM","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":"087e15ed1a778aa79d8784ebee769b29dbf090993dfa6bb5ae5ecf285cd5ca82","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-08-25T15:55:17Z","title_canon_sha256":"93c924961db4d654f55fad1e435576e5bd7ffcc027eaae9e976c9a5769afbfbe"},"schema_version":"1.0","source":{"id":"2508.18148","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.18148","created_at":"2026-07-05T11:58:54Z"},{"alias_kind":"arxiv_version","alias_value":"2508.18148v1","created_at":"2026-07-05T11:58:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18148","created_at":"2026-07-05T11:58:54Z"},{"alias_kind":"pith_short_12","alias_value":"LZ4IQVSE5XFN","created_at":"2026-07-05T11:58:54Z"},{"alias_kind":"pith_short_16","alias_value":"LZ4IQVSE5XFNAPBO","created_at":"2026-07-05T11:58:54Z"},{"alias_kind":"pith_short_8","alias_value":"LZ4IQVSE","created_at":"2026-07-05T11:58:54Z"}],"graph_snapshots":[{"event_id":"sha256:57671d15ec0618309a83f275cc47c2ecbd656679b0d02f8973d057060fc9f583","target":"graph","created_at":"2026-07-05T11:58:54Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2508.18148/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Intrusion Detection Systems (IDS) play a crucial role in network security defense. However, a significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples. To address these issues, this paper introduces a novel semi-supervised framework \\textbf{GANGRL-LLM}, which integrates Generative Adversarial Networks (GANs) with Large Language Models (LLMs) to enhance malicious code generation and SQL Injection (SQLi) detection capabilities in few-sample learning scenarios. Specifically, our framework adopts a collaborative training paradigm where: (1)","authors_text":"Daizong Liu, Haijian Ma, Pan Zhou, Xiaowen Cai, Yulai Xie","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-08-25T15:55:17Z","title":"Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18148","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":"sha256:0a3e8ff7764c6a2ac69a91e9b77a247d63430e6e3119c7f81446c9f8121a904c","target":"record","created_at":"2026-07-05T11:58:54Z","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":"087e15ed1a778aa79d8784ebee769b29dbf090993dfa6bb5ae5ecf285cd5ca82","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2025-08-25T15:55:17Z","title_canon_sha256":"93c924961db4d654f55fad1e435576e5bd7ffcc027eaae9e976c9a5769afbfbe"},"schema_version":"1.0","source":{"id":"2508.18148","kind":"arxiv","version":1}},"canonical_sha256":"5e78885644edcad03c2e92373f8b1a8300ed41510437491cd7bfbd21a4657a7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5e78885644edcad03c2e92373f8b1a8300ed41510437491cd7bfbd21a4657a7b","first_computed_at":"2026-07-05T11:58:54.487377Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:58:54.487377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2gm+sOmOALi0Bs79/wsA68kMGFuGR6qYvV7NSYoQIkoxNsLcxkYAEw5ouMCbuk8k/S2pRSSzwJkJtW9UmDaEDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:58:54.487790Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.18148","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a3e8ff7764c6a2ac69a91e9b77a247d63430e6e3119c7f81446c9f8121a904c","sha256:57671d15ec0618309a83f275cc47c2ecbd656679b0d02f8973d057060fc9f583"],"state_sha256":"0c3de2427c979cef99f082e761496aa7645714e4bb8c3ee4955e2c0bb7f7e24d"}