IoT Botnet Detection framework from Network Behavior based on Extreme Learning Machine
Nasimul Hasan, Zhenxiang Chen, Chuan Zhao, Yuhui Zhu, Cong Liu
Abstract
IoT devices have been affected by fundamental security flaws in recent years, rendering them exposed to various threats and viruses, particularly IoT botnets. In contrast to conventional malware on desktop computers and Android, heterogeneous processor architecture constraints on IoT devices pose various challenges to researchers. Traditional methodologies are challenging to apply because of the IoT’s unique properties, such as resource-constrained devices, enormous volumes of data, and the requirement of real-time detection. Then it proposes a lightweight framework to detect IoT botnet and botnet families. The framework operates with bot behavior data over a simple yet effective learning based method named Extreme Learning Machine. For IoT botnet detection, the experimental results demonstrate that the suggested technique achieves accuracy, precision, and recall of 97.7%, 97.1%, and 97.1%, respectively. The detection performance of botnet families is inspiring. Furthermore, a comparison of our framework to other current approaches reveals that it produces better results, particularly in terms of the training time, which gives it a considerable edge over other learning-based methods.