Analysis of Bayesian classification‐based approaches for Android malware detection. Issue 1 (1st January 2014)

• Record Type: Journal Article
• Authors: Yerima, Suleiman Y.; Sezer, Sakir; McWilliams, Gavin
• Is Part Of: IET information security. Volume 8:Issue 1(2014)
• Page Start: 25
• Page End: 36
• Publication Date: 2014-01-01
• Subjects: invasive software -- learning (artificial intelligence) -- operating system kernels -- pattern classification -- smart phones; Android malware detection -- mobile malware -- smartphones -- signature based scanning -- machine learning -- static analysis -- static analytic Bayesian classification; Computer security -- Periodicals; Cryptography -- Periodicals; Computer networks -- Security measures -- Periodicals; Database security -- Periodicals; 005.8
• DOI: 10.1049/iet-ifs.2013.0095
• Languages: English
• ISSNs: 1751-8709

Abstract

Abstract : Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature‐based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero‐day Android malware. Hence, the authors develop and analyse proactive machine‐learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static‐analytic Bayesian classification‐based solutions for detecting unknown Android malware.