A machine learning approach for linux malware detection
K. A. Asmitha, P. Vinod · 2014
The increasing number of malware is becoming a serious threat to the private data as well as to the expensive computer resources. Linux is a Unix based machine and gained popularity in recent years. The malware attack targeting Linux has been increased recently and the existing malware detection methods are insufficient to detect malware efficiently. We are introducing a novel approach using machine learning for identifying malicious Executable Linkable Files. The system calls are extracted dynamically using system call tracer Strace. In this approach we identified best feature set of benign and malware specimens to built classification model that can classify malware and benign efficiently. The experimental results are promising which depict a classification accuracy of 97% to identify malicious samples.