GAN-AE: An unsupervised intrusion detection system for MQTT networks

Households and businesses are becoming increasingly reliant on IoT systems since they can reliably automate many tasks that otherwise require extensive human involvement. As a result, the number of IoT devices that are being added to the Internet is increasing steeply. To cater to the increasing demand and attract customers, manufacturers are focusing on producing cheaper and easy-to-use IoT devices without giving enough importance to the security aspect. With the recent developments in machine learning algorithms, IoT device manufacturers have started using supervised intrusion detection models to protect IoT systems from security breaches. However, these systems are being targeted by novel attacks that cannot be detected by the already deployed supervised models. Thus, it is important to develop solutions to protect the IoT networks/systems from these previously unknown intrusions. MQTT is a widely used network protocol in IoT systems due to its lightweight and flexible nature. In this paper, we propose a novel unsupervised GAN and autoencoder-based model, called GAN-AE, for detecting unknown intrusions in MQTT IoT applications. The performance of the proposed GAN-AE model is compared with other popular unsupervised models namely autoencoder, One-Class SVM (OCSVM), and Isolation Forest (IF). The GAN-AE model performed superior to other models with accuracy, F1-Score of 0.97 on our custom-built and public MQTT dataset.