A novel parallel classifier scheme for vulnerability detection in Android. (July 2019) - Garg, Shivi; Baliyan, Niyati
Abstract: Android is one of the most commonly used mobile operating systems; however, its open-source nature and flexibility of usage attract a lot of attention from cybercriminals. In recent years, the rapid increase in malware has become a major cause of concern amongst Android users. The cybercriminals either aim to exploit confidential information from users or try to corrupt their systems by infecting them with malicious code. In order to make Android systems more secure, several malware detection techniques using static, dynamic, and hybrid analysis have been introduced in recent times; however, these techniques are inaccurate and have low efficiency. The paper not only explains how distinctive parallel classifiers can be used for detecting zero-day android malware but also addresses the oncoming highly elusive vulnerabilities. The proposed methodology combines characteristics from various parallel classifiers using expectation maximization to achieve 98.27% accuracy.