Adversarial Attacks on Machine Learning–Based Cybersecurity Classifiers: A Systematic Analysis and Defense Framework

Abstract

Machine learning and deep learning have become essential components of modern cybersecurity because of their ability to detect malicious activities, classify network traffic, identify malware, recognize phishing attempts, and support automated incident response. However, machine learning–based cybersecurity classifiers are vulnerable to adversarial attacks in which attackers deliberately manipulate data, features, model inputs, or training processes to cause misclassification or evade detection. This study systematically analyzes adversarial attacks against machine learning–based cybersecurity classifiers and proposes a comprehensive defense framework to improve their robustness and reliability. The study examines major attack categories, including evasion, data poisoning, model extraction, inference, backdoor, and adversarial example attacks. It also analyzes attack surfaces, threat models, and the consequences of adversarial manipulation in intrusion detection, malware detection, phishing classification, and other AI-enabled cybersecurity systems. The findings indicate that adversarial attacks can significantly reduce detection performance, increase false-negative and false-positive rates, manipulate decision boundaries, and undermine trust in automated security systems. A defense-in-depth framework is proposed, incorporating secure data management, adversarial training, robust feature engineering, model validation, ensemble learning, anomaly detection, explainable AI, continuous monitoring, human oversight, and regular security auditing. The study concludes that no single defense mechanism can provide complete protection against adversarial machine learning attacks. Therefore, resilient AI-based cybersecurity requires a layered approach that protects data, features, models, inference processes, and the entire machine learning lifecycle.