Malware Collusion Attack against SVM: Issues and Countermeasures

Abstract

Android has become the most popular mobile platform, and a hot target for malware developers. At the same time, researchers have come up with numerous ways to deal with malware. Among them, machine learning based methods are quite effective in Android malware detection, the accuracy of which can be as high as 98%. Thus, malware developers have the incentives to develop more advanced malware to evade detection. This paper presents an adversary attack scenario (Collusion Attack) that will compromise current machine learning based malware detection methods, especially Support Vector Machines (SVM). The malware developers can perform this attack easily by splitting malicious payload into two or more apps. Meanwhile, attackers may hide their malicious behavior by using advanced techniques (Evasion Attack), such as obfuscation, etc. According to our simulation, 87.4% of apps can evade Linear SVM by Collusion Attack. When performing Collusion and Evasion Attack simultaneously, the evasion rate can reach 100% at a low cost. Thus, we proposed a method to deal with this issue. This approach, realized in a tool, called ColluDroid, can identify the collusion apps by analyzing the communication between apps. In addition, it can integrate secure learning methods (e.g., Sec-SVM) to fight against Evasion Attack. The evaluation results show that ColluDroid is effective in finding out the collusion apps and ColluDroid-Sec-SVM has the best performance in the presence of both Collusion and Evasion Attack.

Keywords

• Android security
• machine learning
• SVM
• collusion attack
• evasion attack