A Heuristic Research on Detecting SuspiciousMalware Pattern in Mobile Environment

Abstract

Abstract—with the increasing trend of network technology in mobile platform is becoming more easily accessible and available device to people which an effect of spending valuable amount of time in online social networks which leads to increase of crime. However, due to the increasing technology advanced and popularity of mobile devices, cyber criminals are targeting on mobile device platforms for potential information from victims smart phones. There are billions of malware attacks taking place in every environment (Mobile, IoT, Wireless sensor Network, Cloud). The victims are tempted to use more internets where the device can be compromised by phishing websites and also by malware propagation. In this work, we compared the performance measures by using four machine learning algorithms and one neural network algorithm with aim of identifying mobile malware. With the goal of achieving detection of malware capability, we evaluated the accuracy of the system in terms of Accuracy, Precision, Recall and F1-Score with algorithms. The result shows that neural network algorithm achieved more malware detection accuracy compared with others