A Comprehensive Analysis of Adversarial Attacks Against Android’s Malware Detection Models

Android malware has emerged as a major threat to mobile device security. While machine learning techniques have proven effective in detecting malicious applications, they remain vulnerable to attacks based on adversarial examples. This study investigates the robustness of five widely-used classifiers (Random Forest, Logistic Regression, Support Vector Machines, Naïve Bayes, and K-Nearest Neighbors) against adversarial machine learning in malware classification. Through systematic evaluation using a gradient-based perturbation method, the analysis reveals critical vulnerabilities where adversarial samples cause misclassifications, particularly converting malware into benign classifications. Random Forest and K-Nearest Neighbors exhibited greater robustness compared to other classifiers, but their accuracy still degraded under high-severity attacks. Linear models exhibit more severe degradation, with Logistic Regression showing consistent vulnerability to adversarial perturbations across all malware families. The results demonstrate that current detection systems can be dangerously deceived by adversarial examples, potentially allowing malicious applications to evade detection.