Detection and robustness evaluation of android malware classifiers

Abstract

Android malware attacks are tremendously increasing, and evasion techniques become more and more effective. For this reason, it is necessary to continuously improve the detection performances. With this paper, we wish to pursue this purpose with two contributions. On one hand, we aim at evaluating how improving machine learning-based malware detectors, and on the other hand, we investigate to which extent adversarial attacks can deteriorate the performances of the classifiers. Analysis of malware samples is performed using static and dynamic analysis. This paper proposes a framework for integrating both static and dynamic features trained on machine learning methods and deep neural network. On employing machine learning algorithms, we obtain an accuracy of 97.59% with static features using SVM, and 95.64% is reached with dynamic features using Random forest. Additionally, a 100% accuracy was obtained with CART and SVM using hybrid attributes (on combining relevant static and dynamic features). Further, using deep neural network models, experimental results showed an accuracy of 99.28% using static features, 94.61% using dynamic attributes, and 99.59% by combining both static and dynamic features (also known as multi-modal attributes). Besides, we evaluated the robustness of classifiers against evasion and poisoning attack. In particular comprehensive analysis was performed using permission, APIs, app components and system calls (especially n-grams of system calls). We noticed that the performances of the classifiers significantly dropped while simulating evasion attack using static features, and in some cases 100% of adversarial examples were wrongly labelled by the classification models. Additionally, we show that models trained using dynamic features are also vulnerable to attack, however they exhibit more resilience than a classifier built on static features.