We present a practical implicit authentication system for Virtual Reality (VR) that uses natural interaction tasksgrabbing, pointing, and typing-as behavioral biometrics. The system extracts 221 features from head-mounted and controller sensors and is trained as a lightweight SVM-based binary classifier using data from legitimate users and a small set of reference users to simulate attacker behavior. In a 24-participant study, our system achieved strong authentication performance, with median Equal Error Rates (EERs) of 0.4% for grabbing, 2.6 % for pointing, and 0.3 % for typing. Designed for on-device deployment, it requires no GPU support, completes inference within 1 second, and maintains a compact model size under 0.2 MB, enabling efficient, real-time authentication on standalone VR headsets. Security evaluations with attacker-in-the-loop experiments across no-knowledge, shoulder-surfing, and videoreplay conditions revealed clear trade-offs. Typing and pointing offered strong resistance to impersonation, while grabbing, despite high usability, was more vulnerable under video replay with a $23.8 \%$ attack success rate. These results demonstrate that secure, accurate, and real-time implicit authentication is feasible in VR, with task-specific characteristics enabling flexible deployment based on security and usability needs.

Conventional ad blocking and tracking prevention tools often fall short in addressing web content manipulation. Machine learning approaches have been proposed to enhance detection accuracy, yet aspects of practical deployment have frequently been overlooked. This paper introduces AdFlush, a novel machine learning model for real-world browsers. To develop AdFlush, we evaluated the effectiveness of 883 features, ultimately selecting 27 key features for optimal performance. We tested AdFlush on a dataset of 10,000 real-world websites, achieving an F1 score of 0.98, thereby outperforming AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84). Additionally, AdFlush significantly reduces computational overhead, requiring 56% less CPU and 80% less memory than AdGraph. We also assessed AdFlush's robustness against adversarial manipulations, demonstrating superior resilience with F1 scores ranging from 0.89 to 0.98, surpassing the performance of AdGraph and WebGraph, which recorded F1 scores between 0.81 and 0.87. A six-month longitudinal study confirmed that AdFlush maintains a high F1 score above 0.97 without the need for retraining, underscoring its effectiveness.