首页|New Support Vector Machines Study Findings Have Been Reported by Investigators a t University of Monastir (Attack Detection In Iot Network Using Support Vector M achine and Improved Feature Selection Technique)
New Support Vector Machines Study Findings Have Been Reported by Investigators a t University of Monastir (Attack Detection In Iot Network Using Support Vector M achine and Improved Feature Selection Technique)
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Researchers detail new data in Support Vector Machines. According to news originating from Monastir, Tunisia, by NewsR x correspondents, research stated, "As a result of the rapid advancement of tech nology, the Internet of Things (IoT) has emerged as an essential research questi on, capable of collecting and sending data through a network between linked item s without the need for human interaction. However, these interconnected devices often encounter challenges related to data security, encompassing aspects of con fidentiality, integrity, availability, authentication, and privacy, particularly when facing potential intruders." Our news journalists obtained a quote from the research from the University of M onastir, "Addressing this concern, our study propose a novel host-based intrusio n detection system grounded in machine learning. Our approach incorporates a fea ture selection (FS) technique based on the correlation between features and a ra nking function utilizing Support Vector Machine (SVM). The experimentation, cond ucted on the NSL-KDD dataset, demonstrates the efficacy of our methodology. The results showcase superiority over comparable approaches in both binary and multi -class classification scenarios, achieving remarkable accuracy rates of 99.094% and 99.11%, respectively."
MonastirTunisiaCybersecurityEmergi ng TechnologiesMachine LearningSupport Vector MachinesVector MachinesUni versity of Monastir
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