IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT
Kim-Hung Le, Minh-Huy Nguyen, Trong-Dat Tran, Ngoc-Duan Tran
Abstract
The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for us; on the other hand, the devices are susceptible to various cyber-attacks due to the lack of solid security mechanisms and hardware security support. In this paper, we present IMIDS, an intelligent intrusion detection system (IDS) to protect IoT devices. IMIDS’s core is a lightweight convolutional neural network model to classify multiple cyber threats. To mitigate the training data shortage issue, we also propose an attack data generator powered by a conditional generative adversarial network. In the experiment, we demonstrate that IMIDS could detect nine cyber-attack types (e.g., backdoors, shellcode, worms) with an average F-measure of 97.22% and outperforms its competitors. Furthermore, IMIDS’s detection performance is notably improved after being further trained by the data generated by our attack data generator. These results demonstrate that IMIDS can be a practical IDS for the IoT scenario.