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IEEE Final Year Project Topic for IT

Base Paper Title

Android Malware Detection using Complex Flows

Our Title

IEEE Project Abstract

This paper proposes a new technique to detect mobile malware based on information flow analysis. Our approach examines the structure of information flows to identify patterns of behavior present in them and which flows are related, those that share partial computation paths. We call such flows Complex-Flows, as their structure, patterns, and relations accurately capture the complex behavior exhibited by both recent malware and benign applications. N-gram analysis is used to identify unique and common behavioral patterns present in Complex-Flows. The N-gram analysis is performed on sequences of API calls that occur along Complex-Flows' control flow paths. We show the precision of our technique by applying it to different data sets totaling 7,798 apps. These data sets consist of both recent and older generation benign and malicious apps to demonstrate the effectiveness of our approach across different generations of apps.This paper proposes a new technique to detect mobile malware based on information flow analysis. Our approach examines the structure of information flows to identify patterns of behavior present in them and which flows are related, those that share partial computation paths. We call such flows Complex-Flows, as their structure, patterns, and relations accurately capture the complex behavior exhibited by both recent malware and benign applications. N-gram analysis is used to identify unique and common behavioral patterns present in Complex-Flows. The N-gram analysis is performed on sequences of API calls that occur along Complex-Flows' control flow paths. We show the precision of our technique by applying it to different data sets totaling 7,798 apps. These data sets consist of both recent and older generation benign and malicious apps to demonstrate the effectiveness of our approach across different generations of apps.

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