Android Malware Detection Technology Based on Improved Bayesian Classification
Yu Lu, Zulie Pan, Liu Jingju, Shen Yi · 2013
Emerging feathers of mobile devices have given new threats to the mobile phone security, which makes malware detection technology becoming more and more necessary. Android is one of the newer operating systems based on Linux kernel and in this way it is more vulnerable to attacks. In this paper, we proposed a new Android malware detection method. It can monitor various features obtained from Android mobile device and then applies machine learning technology to classify the mobile applications as benign or malicious. Also we make improvements on Naïve Bayesian Classification method combined with Chi-Square filtering test. Experiments suggest that the classification method is effective in detecting Android malware.