{"data":{"id":"8b30939f-1a34-45c2-97b8-cff97c0d28b5","title":"No Time to Evade: Context-Aware Conditional Entropy Partitioning and Model Fusion for Robust Multiprocess Malware Detection","summary":"This research addresses a security challenge where malware (malicious software) uses polymorphic evasion tactics, splitting its behavior across multiple processes (concurrent program instances) to evade detection systems. The authors propose a defense framework that uses entropy-based partitioning (dividing system activity logs by information density) and model fusion (combining multiple AI models trained differently) to better detect such multi-process malware, showing improved detection accuracy even when malware attempts advanced evasion techniques.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11457734","publishedAt":"2026-03-30T13:17:50.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"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":"2026-03-30T13:17:50.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}