A Robust Malware Detection Approach for Android System Against Adversarial Example Attacks

In recent years, Android has become the leading smartphone operating system across the world. However, due to their increasing popularity, Android devices have become the primary target to mobile malware. To address the arising security threats, many malware detection approaches have been studied that aim at providing strong defense mechanisms against malware. However, with more such malware detection systems being distributed and deployed, malware authors tend to generate adversarial examples by manipulating mobile applications to avoid being detected by the malware detection systems. In this paper, we investigate different types of adversarial example attacks while researching a viable approach to fight against them. More specifically, we first present the literature review on both existing malware detection approaches and adversarial example attacks against them. Then, we focus on the widely used evasion attack model that is applied to generate mutated samples. By working with various app features such as binary N-grams of API calls, we will generate feature sets consisting of a selected range of binary N-grams. As a result, we intend to use the manipulated dataset to develop and train our classifier to detect the evasion attack, and the goal of our approach is to further enhance the robustness of malware detection approach in the presence of adversarial example attacks.