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

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

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

Software Defined Internet of Things (SD-IoT) is currently developed extensively. The architecture of the Software Defined
Network (SDN) 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 major problem in SD-IoT networks, because
they can overwhelm centralized control systems or controllers. Therefore, a system is needed that can identify and detect these
attacks comprehensively. In this paper, the authors built an LRDDoS detection system using the Random Forest (RF) algorithm
as the classification method. The dataset used during the experiment was considered as a new dataset schema that had 21
features. The dataset was selected using feature importance - logistic regression with the aim of increasing the classification
accuracy results as well as reducing 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 accuracy results increased.

Wahyuli Dwiki Nanda1
, Fauzi Dwi Setiawan Sumadi2

, University of Muhammadiyah Malang

: 20-04-2022

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