Approach for Predicting Mobile Malware

Mobile malware is increasing the number of malicious behaviours such as capturing users' information, delivering subscription SMS, and making calls to premium lines without their awareness. Several studies have explored strategies to counter these assaults in order to build an effective detection method. In the world of security, mobile security has been a quickly rising field. The demand for mobile security has risen considerably in recent years as the number of mobile devices and applications has grown. To deal with enormous no. of harmful mobile apps, a variety of malware detection techniques are reported in various studies. In this research, we offer hybrid method for determining most optimal parameters for detecting mobile malware. We developed a system for predicting mobile malware using a hybrid of ANFIS and NIMOA in our hybrid method. In compared to previous optimization methods, evaluations using real-world datasets illustrate the usefulness of our approach.