{"data":{"id":"09691b55-aec9-41e3-9713-3acd9e271b11","title":"Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation","summary":"This paper proposes a KPCA-based framework for out-of-distribution detection, learning a discriminative non-linear subspace from in-distribution features and using reconstruction error on that subspace to separate InD and OoD data. The authors select a Cosine-Gaussian kernel, motivated by two non-linear patterns linked to the InD-OoD disparity, and approximate it cheaply, with the approximation weighted by InD data confidence to scale to large datasets.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11573215","publishedAt":"2026-06-22T13:17:17.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-06-22T13:17:17.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.8,"researchCategory":"peer_reviewed","atlasIds":null}}