{"data":{"id":"e634f920-3c4c-499e-be07-3b06fde17f42","title":"Towards few-shot malware classification with fine-grained and pattern-aware multi-prototype network","summary":"This research paper proposes FIPAPNet, a machine learning system designed to classify malware when only a few samples are available, which is important because new malware variants often appear with limited examples. The system uses few-shot learning (a technique where AI learns from minimal training data) combined with dynamic features like system call sequences to achieve 93% accuracy in early-stage malware detection. This approach helps security defenders respond quickly to zero-day attacks (new, previously unknown malware) without needing hundreds of samples to retrain their detection models.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"https://www.sciencedirect.com/science/article/pii/S2214212626000487?dgcid=rss_sd_all","publishedAt":"2026-03-16T20:12:19.541Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}