Fog-Based Ransomware Detection for Internet of Medical Things Using Lighweight Machine Learning Algorithms

Instances of severe cyber threats such as aggressive attacks, malware, and ransomware have been causing significant harm to computer systems, servers, and various applications across diverse industries and enterprises. These security issues are of paramount importance and require immediate attention. To address these concerns, it is crucial to detect and classify ransomware effectively for prompt response and prevention. This research employs deep learning algorithms to achieve this goal by applying three methods which are DNN, LSTM and Bi-LSTM. The approach involves analyzing the behavior patterns of ransomware and identifying distinctive features that can differentiate between various types of ransomware families. The performance of the models is assessed using a dataset containing instances of ransomware attacks, demonstrating their capability to accurately detect and classify ransomware. Essentially, the study aims to enhance cybersecurity measures by leveraging advanced techniques in artificial intelligence to combat the growing threats posed by ransomware attacks.