@Article{, title={An Internet of Things Botnet Detection Model Using Regression Analysis and Linear Discrimination Analysis}, author={Manar J. Gatea and Sarab M. Hameed}, journal={Iraqi Journal of Science المجلة العراقية للعلوم}, volume={63}, number={10}, pages={4534-4546}, year={2022}, abstract={The Internet of Things (IoT) has become a hot area of research in recent yearsdue to the significant advancements in the semiconductor industry, wirelesscommunication technologies, and the realization of its ability in numerousapplications such as smart homes, health care, control systems, and military.Furthermore, IoT devices inefficient security has led to an increase cybersecurityrisks such as IoT botnets, which have become a serious threat. To counter this threatthere is a need to develop a model for detecting IoT botnets.This paper's contribution is to formulate the IoT botnet detection problem andintroduce multiple linear regression (MLR) for modelling IoT botnet features withdiscriminating capability and alleviating the challenges of IoT detection. In addition,a linear discrimination analysis (LDA) model for distinguishing between normalactivities and IoT botnets was developed.Network-based detection of IoT (N-BaIoT) dataset was used to evaluate theperformance of the proposed IoT botnet detection model in terms of accuracy,precision, and detection rate. Experimental results revealed that the proposed IoTbotnet detection model provides a relevant feature subset and preserves highaccuracy when compared with state-of-the-art and baseline methods, particularlyLDA.

} }