MALWARE DETECTION USING DATA ANALYTICS APPROACH OVER ANDROID ARCHITECTURE
Keywords:
Mobile Computing; Malware; malware detection; Mobile architecture; Android antivirusAbstract
Detection and prevention of malware on the mobile platform is a current time requirement. Various antimalware frameworks were presented to reduce cyber-attack over mobile devices. Malware persist particular features or
structure which make changes in software or users data. Therefore, an appropriate approach is needed that can improve
the security of mobile devices. In this paper, we conduct a study of various malware detection techniques. In a recent
Paper authors have given MADAM (Multi-Level Anomaly Detection for Android Malware) approach for malware
detection in Android system and architecture, where they investigated incoming application installation, running
application behavior learning. MADAM approach help in proper anomaly detection utilization, also previously given
approaches which work either behavior based or pattern based in discussed. This paper overall discuss about the
technique overview and scenario associate with them.
