LRDDoS Attack Detection on SD-IoT Using Random Forest with Logistic Regression Coefficient

Keywords: LRDDoS, SD-IoT, Random Forest, Logistic-Regression, Machine Learning


Software-Defined Internet of Things (SD-IoT) is currently developed extensively. The Software-Defined Network (SDN) architecture allows Internet of Things (IoT) networks to separate control and data delivery areas into different abstraction layers. However, Low-Rate Distributed Denial of Service (LRDDoS) attacks are a significant problem in SD-IoT networks because they can overwhelm centralized control systems or controllers. Therefore, a system is needed to identify and detect these attacks comprehensively. This paper built an LRDDoS detection system using the Random Forest (RF) algorithm as the classification method. The dataset used during the experiment was considered a new dataset schema with 21 features. The dataset was selected using feature importance - logistic regression to increase the classification accuracy results and reduce the computational burden of the controller during the attack prediction process. The results of the RF classification with the LRDDoS packet delivery speed of 200 packets per second (PPS) had the highest accuracy of 98.7%. The greater the delivery rates of the attack pattern, the increased accuracy results.



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How to Cite
Nanda, W. D., & Sumadi, F. D. S. (2022). LRDDoS Attack Detection on SD-IoT Using Random Forest with Logistic Regression Coefficient. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(2), 220 -226.
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