Android Malware Detection Using Step-Size Based Multi-layered Vector Space Models.

With computer software becoming more important and prolific in today's world, malicious software (malware) continues to be one of its greatest security threats. Alongside this trend, smartphones and mobile devices have become the prominent method for accessing the Internet and its vast resources of information and business applications. With the amount and variety of Android based devices increasing daily, the need for better and more accurate malware detection approaches for the Android platform also increases. In this paper, we explore whether a data mining technique originally developed to detect malware on a Windows operating system can be utilized to detect malware in Android mobile devices. In addition, we propose a novel algorithm for detecting malware on Android that relies on step sizes and a simplified multi-layer vector space (MLVS) model. We compare the effectiveness of these two techniques, with the goal of determining optimal step sizes for our modified MLVS (MMLVS) approach to detect Android malware. Our results show that the two methods are able to correctly classify the samples as malware or uninfected with strong accuracy. In addition, we identify key elements that need to be address to permit further improvement within Android environments.