The response time on a Web application really determines the convenience of its users. more Purpose Internet users and Web-based applications continue to grow every day. Purpose Internet users and Web-based applications continue to grow every day. The experiment result indicates that the proposed architecture can. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. Moreover, many existing rule-based detection systems can be circumvented by attackers. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. It was said that most of the attacks in IoT environments are botnet-based attacks.
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