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This paper argues that conventional logging and network-centric security situational awareness miss an important gap: they can tell you that an identity logged in or a token was used, but not wheth…
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This paper asks a very specific question that many continuous-authentication systems sidestep: if gaze is a strong biometric today, does it still work months later in VR? The authors evaluate eye-t…
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This paper addresses the challenge of generating synthetic face datasets with high intra-class diversity while preserving identity, to train face recognition (FR) models without relying on privacy-…
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This paper addresses the critical challenge of 3D human pose estimation under real-world deficiency-aware conditions such as occlusions, noise interference, and missing viewpoints
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This paper conducts a comparative empirical study of three statistical keystroke dynamics authentication methods—Gaussian Mixture Models (GMM), Mahalanobis Distance, and Gunetti Picardi’s Distance …
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This paper addresses the significant privacy threat posed by browser fingerprinting, which enables cross-site user tracking and identification beyond traditional cookie-based methods
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This paper addresses the problem of online learning to rank under the cascade model in the presence of adversarial corruptions manipulating user feedback
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This paper is a descriptive case study of the October 2023 23andMe breach, focused on how a relatively small credential-stuffing event escalated into a much larger exposure because of the platform’…
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This paper addresses a critical gap in the measurement and detection of browser fingerprinting, an invasive tracking technique used for profiling users online
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This paper tackles sybil detection from a graph-algorithms angle rather than as a pure machine-learning classification task
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This paper tackles a very specific bottleneck in forehead-crease biometrics: there are too few real identities and too little within-subject variation to train a verification system that generalize…
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This paper asks a practical security question that voting-based LLM leaderboards have mostly glossed over: if model identities are hidden during pairwise voting, can an adversary still learn which …
