High accuracy android malware detection using ensemble learning. Issue 6 (1st November 2015)

• Record Type: Journal Article
• Authors: Yerima, Suleiman Y.; Sezer, Sakir; Muttik, Igor
• Is Part Of: IET information security. Volume 9:Issue 6(2015)
• Page Start: 313
• Page End: 320
• Publication Date: 2015-11-01
• Subjects: invasive software -- Android (operating system) -- learning (artificial intelligence); ensemble machine learning -- static analysis -- high accuracy Android malware detection; Computer security -- Periodicals; Cryptography -- Periodicals; Computer networks -- Security measures -- Periodicals; Database security -- Periodicals; 005.8
• DOI: 10.1049/iet-ifs.2014.0099
• Languages: English
• ISSNs: 1751-8709

Abstract

Abstract : With over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity among smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature‐based methods become less potent in detecting unknown malware, alternatives are needed for timely zero‐day discovery. Thus, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3–99% detection accuracy with very low false positive rates.