ANDROID MALWARE DETECTION USING MACHINE LEARNING

ANDROID MALWARE DETECTION USING MACHINE LEARNING

Mrs.S..Francis Shamili, Anumala Venkatesh , Gavini Ganesh , Moraboina Thirupati Raju , Muram Thirumaleshwar reddy , Reddimi Manoj Kumar Reddy

Department of Computer Science and Engineering

Dhanalakshmi Srinivasan Engineering College, Perambalur

Abstract— One of the most popular operating systems for mobile devices is Android. Similar to the growth of mobile devices, harmful apps and adware are becoming more and more prevalent. There are several commercial technologies based on signatures that are on the market that may limit the penetration and spread of malicious programmes. According to multiple studies, standard signature-based detection systems are effective up to a point and malware writers use a variety of evasion tactics to get around these tools. Therefore, given the current situation, a replacement for the signature-based system that is very effective at detecting malware is becoming more and more necessary. Machine learning techniques that assess data from harmful apps and utilize those attributes to identify and detect unknown dangerous programmes were the subject of much recent study. In this model, we introduced a brand-new malware detection method based on Android permissions. The use of machine learning methods, such as the Logistic Regression Model, Random Forest, Support Vector Machine and Neural Network, is our last step.