Host-Based Intrusion Detection System for IoT using Convolutional Neural Networks

This paper proposes and analyses a lightweight Convolutional Neural Network (CNN) based anomaly detection framework for Internet of Things (IoT) devices. IoT security has become a massive concern in recent years. IoT devices form the backbone of much of the critical infrastructure we have today. From power stations to biomedical devices, there is the potential of heavy financial damage and loss of human life if they become compromised. As IoT adoption accelerates, the amount of cyberattacks on IoT devices increases substantially. Due to the resource constrained nature of IoT devices, no security solution addresses all concerns in the IoT field. By training models based on normal power consumption behaviour, a wide range of anomalies can be detected in the power time series data of the IoT device. The methodology proposed in this paper is generic in nature, making it applicable to every IoT device on the market. The work in this paper is implemented at the edge, on an ultra-low-power microcontroller.