Robustness Evaluation of AI Security Monitoring Algorithms in Multi-Dimensional Data Flow Environments

Abstract

AI security monitoring algorithms are increasingly deployed to detect malicious activities within complex, multi-dimensional data flow environments. Ensuring the robustness of these algorithms against adversarial attacks and noisy data is crucial for maintaining system integrity. This review paper provides a comprehensive overview of techniques for evaluating the robustness of AI-based security monitoring algorithms specifically designed for multi-dimensional data flow environments. We begin by outlining the challenges associated with securing these environments and the role of AI in enhancing security monitoring capabilities. We then delve into a historical overview of robustness evaluation methods, highlighting their evolution and limitations. The core of the paper focuses on two key themes: adversarial robustness and data quality robustness. Adversarial robustness explores techniques for assessing and improving the resilience of algorithms against adversarial examples, while data quality robustness examines the impact of noisy, incomplete, or biased data on algorithm performance. We critically compare existing evaluation methodologies, emphasizing their strengths, weaknesses, and applicability to different types of AI algorithms and data flow environments. Further, we discuss the prominent challenges in ensuring robustness, such as scalability, transferability, and the need for adaptive evaluation techniques. The review concludes by outlining future research directions, including the development of more robust algorithms, advanced evaluation frameworks, and techniques for explainable robustness. This review will provide researchers and practitioners with a valuable resource for understanding the state-of-the-art in robustness evaluation and for guiding future efforts in developing more secure and reliable AI-based security monitoring systems.