Ransomware Attack Detection in the Internet of Things using Machine Learning Approaches

Abstract

Ransomware is a well-known virus variant that has become increasingly prevalent because of the severe and long-lasting damage it causes to its users. Because ransomware causes irreparable damage, quick detection of these threats is essential. Internet of Things(IoT) networks are the main focus of some of the investigations for the security of the system. Over the past ten years, ransomware intrusions have increased, greatly upsetting enterprises. It is essential to create fresh and improved methods for identifying this kind of virus. In order to recognise the constantly changing ransomware attacks in the IoT environment, the research uses several machine learning(ML) approaches. The paper also includes a description of the dataset that was collected from its sources and used for ransomware detection. The study compares the capabilities of various ML techniques for accurately detecting ransomware attacks in IoT systems. The “Cybersecurity: BookMyShow add URL” dataset has been used to apply Logistic Regression(LR), K-Nearest Neighbour(KNN), and eXtreme Gradient Boosting(XGB) as ML techniques. For the purpose of comparing the aforementioned ML approaches, performance metrics including accuracy, recall, precision, and f1-score have been determined. In comparison to LR and KNN, which had accuracy rates of 92.92% and 94.39%, respectively, the system discovered that XGB had the best accuracy, with a score of 96.62%.