Malware Detection In Mobile Through Analysis of Application Network Behavior By Web Application

This system detects the mobile malware by analyzing suspicious network activities through the traffic analysis. In our system, the detection algorithms which we are using are works as modules inside the Open Flow controller, and the security rules can be imposed in real time. Here, we are using new behavior-based anomaly detection system which is used for identifying meaningful deviations in a mobile application’s network behavior. Here, we are trying to detect a new type of mobile malware with self-updating capabilities. This kind of malware neither identified by using the standard signatures approach nor applying static or dynamic analysis methods. The detection is completely based on the application’s network traffic patterns only. Here we are using Semi-supervised machine-learning techniques for learning the normal behavioral patterns. IndexTerms– Android Mobile Malware, Network Traffic, Machine learning, Smart-Phones Security. ________________________________________________________________________________________________________