首页|Yuan Ze University Researcher Furthers Understanding of Machine Learning (Feature-Selection-Based DDoS Attack Detection Using AI Algorithms)
Yuan Ze University Researcher Furthers Understanding of Machine Learning (Feature-Selection-Based DDoS Attack Detection Using AI Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews - Data detailed on artificial intelligence have bee n presented. According to news originating fromTaoyuan, Taiwan, by NewsRx corre spondents, research stated, “SDN has the ability to transform networkdesign by providing increased versatility and effective regulation.”Funders for this research include Nstc.The news reporters obtained a quote from the research from Yuan Ze University: “ Its programmablecentralized controller gives network administration employees m ore authority, allowing for more seamlesssupervision. However, centralization m akes it vulnerable to a variety of attack vectors, with distributeddenial of se rvice (DDoS) attacks posing a serious concern. Feature selection-based Machine L earning(ML) techniques are more effective than traditional signature-based Intr usion Detection Systems (IDS) atidentifying new threats in the context of defen ding against distributed denial of service (DDoS) attacks.In this study, NGBoos t is compared with four additional machine learning (ML) algorithms: convolution alneural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and R andom Forest, in orderto assess the effectiveness of DDoS detection on the CICD DoS2019 dataset. It focuses on importantmeasures such as F1 score, recall, accu racy, and precision. We have examined NeTBIOS, a layer-7 attack,and SYN, a laye r-4 attack, in our paper.”
Yuan Ze UniversityTaoyuanTaiwanAsiaAlgorithmsCybersecurityCyborgsEmerging TechnologiesMachine Learning
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