Abstract
Over the past few years, Android has been found to be the most prevalent operating system. The increase in the adoption of Android by users has led to many security issues. The amount of malware targeting Android has significantly increased. Due to the increase in the amount of malware, their detection and classification have become a major issues. Currently, the detection techniques comprise static and dynamic malware analysis. This chapter presents a comparative study of various feature selection methods through machine learning classifiers for Android malware classification. The study examines the features acquired through static malware analysis (such as command strings, permissions, intents, and API calls), and various feature selection techniques are employed to find suitable features for classifying malware to carry out the comparative analysis. The experimental results illustrate that the gain ratio feature selection approach selects relevant features for the classification of Android malware and provides an accuracy of 97.74%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.