IoT Botnet Classification using CNN-based Deep Learning
Nahid Ebrahimi Majd, Dhatri Sai Kumar Reddy Gudipelly · 2023
The size and scope of using IoT has been rapidly growing in the past few years. This growth rises security challenges in networks. One of the pressing concerns is detecting the type of attacks emanated from IoT devices. To tackle this issue, machine learning solutions have been proposed that classify the IoT traffic. Most of the current solutions on IoT botnet attack family classification propose a separate model for each IoT device. This is not a suitable approach for an IoT ecosystem where a variety of IoT devices are used and new device types are introduced every day. In this research, we propose a united model that classifies the traffic from any type of IoT device. Such approach is especially essential in IoT as a large number of different devices could be infected to be used in large-scale botnet attacks. We propose CNN-based deep learning family classification models, which classify the IoT traffic to benign and different types of botnet attacks. We trained and tested our models using N-BaIoT dataset, which contains data for benign traffic and 10 types of BashLite and Mirai botnet attacks. The experimental results demonstrate that our models outperform the related proposed models in terms of accuracy, precision, and recall.