Adversarial Attacks against Machine Learning-Based Security Systems: A Growing Threat

Abstract

Machine learning (ML) has become a central component of modern cybersecurity, powering systems for intrusion detection, malware analysis, spam filtering, and anomaly detection. However, as these systems grow more intelligent, they have also become susceptible to a new and rapidly evolving class of threats known as adversarial attacks. These attacks exploit the vulnerabilities inherent in ML algorithms, manipulating input data to deceive models into making incorrect or harmful decisions. From evading malware classifiers to bypassing facial recognition and spam filters, adversarial attacks challenge the reliability and robustness of AI-driven security mechanisms. This paper examines the mechanisms behind adversarial attacks, their impact on machine learning-based security infrastructures, and emerging defense strategies such as adversarial training, model hardening, and explainable AI. By analyzing both the offensive and defensive dimensions, this study underscores the urgent need for resilient AI architectures capable of adapting to an adversarially intelligent threat landscape.