Smart and Cost-effective system for Ransomware identification, detection, and prevention for IoT Enterprise Networks

Abstract

As there is an increase of lightweight systems such as smartphones, on the Internet of Things (IoT) paradigm, it is important to strengthen security and prevent ransomware attacks from taking place. Ransomware attacks have become a significant threat to the security of IoT networks, and their impact can be severe and costly for enterprises. Traditional security mechanisms are no longer valid because of the involvement of resource-constrained systems, which require more computation power and resources. To further enhance ransomware detection capabilities, The proposed system uses a combination of machine learning and network monitoring techniques to detect and prevent ransomware attacks in real-time. This Research Study will address the ransomware attack cybersecurity issues using a state-of-the-art solution approach and develop a smart, effective, and affordable prototype solution for identifying, detecting, and preventing ransomware attacks on IoT enterprise networks. Its combination of machine learning and network monitoring techniques offers a robust defense against the growing threat of ransomware attacks, and its cost-effectiveness makes it accessible to enterprises of all sizes. In this Research Study, we aim to demonstrate the proposed framework, design practical implementation of the system.