ANDROID MALWARE DETECTION USING HAML

Abstract

Abstract- The number of malware is increasing rapidly regardless of the common use of the anti-malware software. To Detect the malware continues to be a challenge as attackers device new techniques to evade from the detection methods. Most of the anti-virus software uses signature-based detection technique which is inefficient in the today’s scenario because of the rapid increase in the number and variants of malware. The signature is a unique identifier for a binary file, which is created by analyzing the binary file using static analysis methods. The dynamic analysis uses the actions and behavior during runtime to find out the type of executable (either malware or benign). Both methods have their own benefits as well as drawbacks. This paper proposes an embedded static and dynamic analysis method to analyses and classifies an unknown executable file. This method uses machine learning technique in which known type of malware and the benign programs are used as training data. By analysis of the binary code and dynamic behavior, the feature vector is selected. The proposed method utilizes the benefits of both static and dynamic analysis thus the efficiency, and the classification result is improved. Our experimental results show an accuracy of 95.8% using static, 97.1% using dynamic and 98.7% using the embedded method. As Compare to the standalone dynamic and static methods, our embedded method gives better accuracy.