Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient

Abstract

The evolution of computer network technology is now experiencing substantial changes, particularly with the introduction of a new paradigm, Software Defined Networking (SDN). The SDN architecture has been applied in a variety of networks, including the Internet of Things (IoT), which is known as SD-IoT. IoT is made up of billions of networking devices that are interconnected and linked to the Internet. Since the SD-IoT was considered as a complex entity, several types of attack on vulnerabilities vary greatly and can be exploited by careless individuals. Low-Rate Distributed Denial of Service (LRDDoS) is one of the availability-based attack that may affect the SD-IoT integration paradigm. Therefore, it is necessary to have an Intrusion Detection System (IDS) to overcome the security hole caused by LRDDoS. The main objective of this research was the establishment of an IDS application for resolving LRDDoS attack using the SVM algorithm combined with the Feature Importance method, namely the Logistic Regression Coefficient. The implemented approach was developed to reduce the complexity or resource’s consumption during the classification process as well as increasing the accuracy. It could be concluded that the Linear kernel SVM algorithm acquired the highest results on the test schemes at 100% accuracy, but the training time required for this model was longer, about 23.6 seconds compared to the Radial Basis Function model which only takes about 1.5 seconds.