TinyIDS - An IoT Intrusion Detection System by Tiny Machine Learning

The use of Internet of Things (IoT) devices in sectors, such as healthcare, automotive, and industrial automation, has increased the risk of attacks against critical assets. Machine learning techniques may be utilized to identify malicious behaviors, but they often require dedicated, energy-intensive, and expensive devices, which may not be deployable in IoT infrastructures. Furthermore, privacy constraints, security policies, and latency constraints could limit the sending of sensitive data to powerful remote servers. To address this issue, the emerging field of TinyML offers a solution for implementing machine learning algorithms directly on resource-constrained devices. Therefore, this article presents the implementation of an intrusion detector, named TinyIDS, which exploits the Tiny machine learning techniques. The detector can be deployed on resource-constrained IoT devices to detect attacks against sensor networks, as well as malicious behaviors of compromised smart objects. On-board training has been exploited to train and analyze data locally without having to transfer sensitive data to remote or untrusted cloud services. The solution has been tested on common MCU-based devices and ToN IoT datasets.