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Erschienen in:
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2015 | OriginalPaper | Buchkapitel

Detection and Mitigation of Android Malware Through Hybrid Approach

verfasst von : Kanubhai Patel, Bharat Buddadev

Erschienen in: Security in Computing and Communications

Verlag: Springer International Publishing

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Abstract

A good number of android applications are available in markets on the Internet. Among them a good number of applications are law quality apps (or malware) and therefore it is difficult for android users to decide whether particular application is malware or benign at installation time. In this paper, we propose a design of system to classify android applications into two classes i.e. malware or benign. We have used hybrid approach by combining application analysis and machine learning technique to classify the applications. Application analysis is performed by both static and live analysis techniques. Genetic algorithm based machine learning technique is used to create rules for creating rule base for the system. The system is tested with applications collected from the various markets on the Internet and two datasets. We have obtained 96.43 % detection rate to classify the applications.

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Vorheriges Kapitel Technical Aspects of Cyber Kill Chain
Nächstes Kapitel Malicious Circuit Detection for Improved Hardware Security
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Metadaten
Titel
Detection and Mitigation of Android Malware Through Hybrid Approach
verfasst von
Kanubhai Patel
Bharat Buddadev
Copyright-Jahr
2015
Buch
Security in Computing and Communications

Print ISBN: 978-3-319-22914-0

Electronic ISBN: 978-3-319-22915-7

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-22915-7_41