MADAM:  EFFECTIVE AND EFFICIENT BEHAVIOR-BASED ANDROID MALWARE DETECTION AND PREVENTION

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

MADAM: EFFECTIVE AND EFFICIENT BEHAVIOR-BASED ANDROID MALWARE DETECTION AND PREVENTION
Author(s):
Asha patel, Almas Safora, Amina Javeriya Sultana, Basharath Sultana, Lokesh Murge
Keywords:

Abstract
Android users are constantly threatened by an increasing number of malicious applications (apps), generically called malware. In this paper we present MADAM, a novel host-based malware detection system for Android devices which simultaneously analyses and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviours. MADAM has been designed to take into account those behaviours characteristics of almost every real malware which can be found in the wild. MADAM detects and effectively blocks more than 96% of malicious apps, which come from three large datasets with about 2,800 apps, by exploiting the cooperation of two parallel classifiers and a behavioural signature-based detector.
Article Details
Unique Paper ID: 149792 Publication Volume & Issue: Volume 7, Issue 1 Page(s): 675 - 678
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