Revealing Mobile Malware

Modern malware uses advanced techniques to hide from static and dynamic analysis tools. To achieve stealthiest when attacking a mobile device, an effective approach is required for the diagnosis of the application. Nowadays current approach based on only analysis on code part or pattern based on which it evaluates an android application for malware detection. The hacker can override this combination of diagnosis of pattern, as a result, which may infect the device with the malware. This paper introduces an approach which is using various techniques like patterns, flow-based, behavior-based, state-based and does an analysis of each individual data by its associated specialized algorithms. The results obtained are fused to get the final results of that application. This paper aims to spot malware using various combinations of algorithms and detect malware. The algorithms that are going to used are Call Graph-Based Classification, HMM model, and Naive Byes Based Classification. Experimental results show the feasibility and effectiveness of the proposed approach to detect malware.