An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems.

ABSTRACT: Machine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity. However, recent studies have revealed their vulnerability to adversarial attacks, where maliciously perturbed traffic samples can deceive trained DNN-based detectors, leading to incorrect classifications and compromised system integrity. While numerous defense mechanisms have been proposed to mitigate these adversarial threats, many fail to achieve a balance between robustness against adversarial attacks, maintaining high detection accuracy on clean data, and preserving the functional integrity of traffic flow features. To address these limitations, this research investigates and integrates a comprehensive ensemble of adversarial defense stra

SUBMITTER: Awad Z 

PROVIDER: S-EPMC12019570 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature