Paper Details

Mobile malware found these days are distributed for financial gain. Most of those malware are created andused as a malware variant that re-uses existing malicious behavior, because the financial objective can be achievedefficiently at low cost, compared with creating new malware. Another reason is that mobile malware with a short lifecycle can be created massively to spread infection. However, anti-malware solutions available these days detect malwareusing the known signature of malware. Therefore, those solutions have a limit in detecting a malware variant thatmodifies existing malware partially. If many malware variants can be detected quickly, infection spread can be blockedin early stages and damages can be reduced. This paper proposes a clustering classification technique based on theunsupervised machine learning algorithm, which is designed to detect malware variants quickly that seek financial gain.

Woong Go, Jun-hyung Park, “A Study on Clustering Classification Technique based on Machine Learning to Detect Android Malware Variants”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 1, pp. 903-908, January 2018.