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cs.CR2025
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee +6
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extract…
cs.CR2025
RADEP: A Resilient Adaptive Defense Framework Against Model Extraction Attacks
Amit Chakraborty, Sayyed Farid Ahamed, Sandip Roy +6
Machine Learning as a Service (MLaaS) enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, the…
cs.LG2025
Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining
Sam Pastoriza, Iman Yousfi, Christopher Redino +4
We propose a novel mechanism for real-time (human-in-the-loop) feedback focused on false positive reduction to enhance anomaly detection models. It was designed for the lightweight…