An Efficient Crypto Based Ransomware Prevention Method in Android Platform

A relatively new kind of intrusion attack called ransomware aims to demand a ransom from its target. Ransomware attacks are carried out by cybercriminals to financially affect victims by damaging their gadgets. Due to Android’s widespread use worldwide, assaults on smartphones using this operating system are growing exponentially. While there are many different kinds of ransomware attacks, this article mainly concentrates on crypto-ransomware as, once the victim’s files are encrypted, it renders data irretrievable. We provided a thorough examination based on machine learning crypto-ransomware detection techniques in this study. This detection must be successful to stop the attack from achieving its objective. In this work, we provide a classification scheme for the security level of crypto-ransomware for detection as well as prevention. This framework includes data collection, preprocessing, feature extraction and selection, or classification, and the application of multiple machine learning techniques. We assessed the accuracy, recall, F1-score, and precision scores using a single Android ransomware dataset. The comparison findings show that the Support Vector Machines (SVM) classifier performs better than competing approaches across all performance metrics, with a 99.5% accuracy rate in detecting crypto Android ransomware. Unlike most previous studies that provide an analysis of crypto-ransomware, this is a thorough assessment that focuses on detection techniques.