MALWARE DETECTION USING DATA ANALYTICS APPROACH OVER ANDROID ARCHITECTURE
| Author(s) | : | Kanchana Binjhade, Dr. Varsha Sharma |
| Institution | : | M.Tech. Scholar, School of Information Technology, UTD-RGPV Bhopal |
| Published In | : | Vol. 5, Issue 3 — March 2018 |
| Page No. | : | 1053-1058 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
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 orstructure which make changes in software or users data. Therefore, an appropriate approach is needed that can improvethe security of mobile devices. In this paper, we conduct a study of various malware detection techniques. In a recentPaper authors have given MADAM (Multi-Level Anomaly Detection for Android Malware) approach for malwaredetection in Android system and architecture, where they investigated incoming application installation, runningapplication behavior learning. MADAM approach help in proper anomaly detection utilization, also previously givenapproaches which work either behavior based or pattern based in discussed. This paper overall discuss about thetechnique overview and scenario associate with them.
Kanchana Binjhade, Dr. Varsha Sharma, “MALWARE DETECTION USING DATA ANALYTICS APPROACH OVER ANDROID ARCHITECTURE”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 3, pp. 1053-1058, March 2018.
