A Fast and Effective Detection of Mobile Malware Behavior Using Network Traffic

Abstract

Android platform has become the most popular smartphone system due to its openness and flexibility. Similarly, it has also become the target of numerous attackers because of these. Various types of malware are thus designed to attack Android devices. All these cases prompted amounts of researchers to start studying malware detection technologies and some of the groups applied network traffic analysis to their detection models. The majority of these models have considered the detection primarily on network traffic statistical features which can distinguish malicious network traffic from normal one. However, when faces a large amount of network traffic on the detection stage, especially some of the network flows are quite huge as a result of containing too many packets, feature extraction can be extremely time consuming. Therefore, we propose a malware detection approach based on TCP traffic, which can quickly and effectively detect malware behavior. We first employ the traffic collection platform to collect network traffic generated by various apps. After preprocessing (filtering and aggregating) the collected network traffic data, we get a large number of TCP flows. Next we extract early packets’ sizes as features from each TCP flow and then send it to detection model to get the detection result. In our method, the time it takes to extract features from 53108 network flows is reduced from 39321 s to 18041 s, which is a reduction of 54%. Meanwhile, our method achieves a detection rate of 97%.