Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

Mobile device has become an essential tool among the community across the globe and has turned into a necessity in daily life. An extensive usage of mobile devices for everyday life tasks such as online banking, online shopping and exchanging e-mails has enable mobile devices to become data storage for users. The data stored in these mobile devices can contain sensitive and critical information to the users. Hence, making mobile devices as the prime target for cybercriminal. To date, Android based mobile devices is one of the mobile devices that are dominating the phone market. Moreover, the ease of use and open-source feature has made Android based mobile devices popular. However, the widely used Android mobile devices has encourage malware author to write malicious application. In a short duration of time mobile malware has rapidly evolve and have the capability to bypass signature detection approach which requires a constant signature update to detect mobile malware. To overcome this drawback an anomaly detection approach can be used to mitigate this issue. Yet, using a single classifier in an anomaly detection approach will not improve the classification detection performance. Based on this reason, this research formulates an ensemble classification method of different n-gram system call sequence features to improve the accuracy of mobile malware detection. This research proposes n-number of classifier models for each different n-gram sequence call feature. The probability output of each classifier is then combined to produce a better classification performance which is better compared to a single classifier

Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201