Adaptive Multi-Layer Defense Framework for Emerging Adversarial Attacks

Abstract

Machine learning models remain highly susceptible to adversarial perturbations, which can cause them to produce incorrect predictions while remaining imperceptible to humans. Existing defense methods usually emphasize either detection or robust training in isolation, which limits their capability to deal with adaptive and previously unseen attacks. In this paper, we propose an Adaptive Multi-Layer Defense Framework that combines input sanitization, statistical drift detection, adversarially trained ensemble learning, randomized smoothing, and online meta-learning within a single pipeline. The framework further employs confidence-weighted decision fusion and continuous threshold adaptation to improve robustness over time. Experimental evaluation on FGSM, PGD, and Carlini-Wagner attacks shows improvement in robust accuracy and a reduction in false-negative rates relative to baseline defenses, providing a scalable and adaptive solution for secure machine learning systems.