vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff12c7200000007d4b050001000800 In recent trends, the mobile devices play a very vital role in day to day activities of human beings. Google Android OS appeared lately i.e., in September 2008 in mobile market and gains more popularity. Google Android OS offers more flexibility for the users by offering N number of free downloadable applications to the users, which in turn gets changed as the superlative target for the attackers . As a result, many android applications that may contain the malware applications which are capable of stealing privacy information of users are available in market as a (.apk) file. The attackers started to target uneducated people and started stealing the information using applications. These applications request user to allow set of permissions during installation. For a new user it is difficult to identify the set of permissions that are harmful. This could be an advantage for malware intruders to access the data or infect the mobile device by introducing malware applications. Therefore, android malware detection various algorithms algorithm and Machine learning approaches is proposed to classify malware and goodware applications by analyzing the permission features.
B.P. Sreejith Vignesh1, M. Rajesh Babu2
Bharathiar University, India1, Karpagam College of Engineering, India2
Android, Malware Application, Principal Component Analysis, Cuckoo Search, Pearson Correlation Coefficient
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 7 , Issue: 1 , Pages: 1344-1349 )
Date of Publication :
October 2016
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