{"data":{"id":"ccc9c71e-ce08-4bfe-a388-cd9b8a018988","title":"Robust Malicious Network Traffic Detection Framework With Automated Drift Detection, Identification, and Adaptation","summary":"Network traffic patterns constantly change, causing traditional malicious traffic detection systems to become less effective over time, a problem called concept drift (when the patterns an AI learned on no longer match real-world data). Researchers developed Argus, a framework that automatically detects when traffic patterns shift, identifies new malicious patterns without human help, and continuously updates itself to maintain high detection accuracy even as attacks evolve.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11523608","publishedAt":"2026-05-18T13:18:18.000Z","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":"2026-05-18T13:18:18.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity","availability"],"aiComponentTargeted":"framework","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}