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Secure Way to Spot Malware in Android Applications using Multi-Classification Technique


Author(s):

Pooja B. Kote , Sir Visvesvaraya Institute Of Technology, Chincholi, Sinnar; Prof. S. M. Rokade, Sir Visvesvaraya Institute Of Technology, Chincholi, Sinnar

Keywords:

malware detection, Naive Byes, Call Graph, android, Neural Network

Abstract:

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. In current approach evaluates the android application for the detection of malware as it performs the analysis part by simple code or the pattern combination. The hacker can override this combination of diagnosis of pattern, as a result which may infect the device with the, malware. This paper introduce approach which is using various techniques like patterns, flow based, behaviour based, state based and do 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 the malware. The algorithms that are going to used are Call Graph Based Classification, NN based Classification, and Naive Byes Based Classification. Experimental results show the feasibility and effectiveness of the proposed approach to detect the malware.


Other Details:

Manuscript Id :IJSTEV3I6013
Published in :Volume : 3, Issue : 6
Publication Date: 01/01/2017
Page(s): 7-10
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