Madam: Effective And Efficient Behavior-based Android Malware Detection And Prevention

Also Available Domains Secure Computing

Project Code :TCREAN19_32

Abstract

Madam: Effective And Efficient Behavior-based Android Malware Detection and Prevention

ABSTRACT

Android users are constantly threatened by an increasing number of malicious applications (apps), generically called malware. Malware constitutes a serious threat to user privacy, money, and device and file integrity. In this paper we note that, by studying their actions, we can classify malware into a small number of behavioral classes, each of which performs a limited set of misbehaviors that characterize them. These misbehaviors can be defined by monitoring features belonging to different Android levels. In this paper we present MADAM, a novel host-based malware detection system for Android devices which simultaneously analyzes and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviors. MADAM has been designed to take into account those behaviors 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 behavioral signature-based detector. Extensive experiments, which also include the analysis of a test bed of 9,804 genuine apps, have been conducted to show the low false alarm rate, the negligible performance overhead and limited battery consumption.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Specifications

12/7 Support, Voice Conference, Video On Demand, Remote Connectivity, Customization, Live Chat Support, Toll Free Support

Demo Video

mail-banner
call-banner
contact-banner
Request Video

Related Projects

Final year projects