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Efficient Android Malware Scanner using Hybrid Analysis

Publication Type : Journal Article

Publisher : International Journal of Recent Technology and Engineering (IJRTE)

Source : International Journal of Recent Technology and Engineering (IJRTE), Volume 7, Issue e-5S3 (2019)

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Campus : Coimbatore

School : School of Engineering

Center : TIFAC CORE in Cyber Security

Department : Computer Science

Verified : Yes

Year : 2019

Abstract : Mobile Malicious applications are great threat to digital world as it is increasing tremendously along with benign applications. Main approaches for analysing the malware are static, dynamic and hybrid analysis. In this paper hybrid analysis is proposed with permission features accessed from applications statically, dynamic features like network activities, file system activities, cryptographic activities, information leakage etc. are dynamically accessed using Android Droid box and dynamic API calls are analysed using API Monitor tool. Separability assessment Criteria is used for relevant feature selection which had improved the performance. In this paper, hybrid features are used to characterize the malware along with learning algorithms such as Naïve Bayes, J48 and Random Forest. Random Forest classifier had produced TPR of 1, FPR of 0 with 77 best features. © BEIESP.

Cite this Research Publication : K. A. Dhanya and Dr. Gireesh K. T., “Efficient Android Malware Scanner Using Hybrid Analysis”, International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. e-5S3, 2019.

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