New Computer Science Findings from University of Sfax Published [Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) for Detecting and Preventing Distributed-Denial-of-Service and Man-in-the-Middle Attacks in...].

A recent report from the University of Sfax discusses the challenges of security in wireless body sensor networks (WBSNs) and the Internet of Medical Things (IoMT) in healthcare technology. The report proposes a solution called the Enhanced Random Forest Classifier with K-Means Clustering (ERF-KMC) algorithm, which combines the accuracy of the enhanced random forest classifier with the clustering capabilities of K-means. This algorithm enhances security in IoMT networks by detecting and categorizing attack messages, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) attacks. The ERF-KMC algorithm also improves network performance metrics and contributes to the reliability and security of IoMT systems. [Extracted from the article]