{"data":{"id":"d50c124d-d4bf-4b0c-beae-6bb75cf7218f","title":"Data-Efficient Cross-Domain Few-Shot Website Fingerprinting With Unsupervised Domain Adaptation","summary":"Website fingerprinting (WF) attacks identify which websites users visit on Tor, a privacy network, but struggle when traffic patterns differ between training and real-world scenarios. This research presents UDA-WF, a new method using unsupervised domain adaptation (a machine learning technique that helps models work across different data distributions) to identify websites more efficiently with less training data. UDA-WF reduces the auxiliary data needed by 95% while maintaining 97.37% accuracy.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11523554","publishedAt":"2026-05-18T13:18:18.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["other"],"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":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.75,"researchCategory":"peer_reviewed","atlasIds":null}}