Autonomous intrusion detection for IoT: a decentralized and privacy preserving approach

Abstract

The Internet of Things (IoT) has been increasingly adopted in domains such as smart infrastructure, healthcare, supply chain, transportation, and many others. However, the constrained computational resources of these devices make conventional security approaches against security threats not applicable. This limitation emphasizes the need to explore new approaches, specifically tailored to these kinds of devices. To increase protection against cyberattacks in IoT devices, Intrusion Detection Systems (IDSs) are considered an effective approach. Machine Learning (ML) techniques can be combined with Federated Learning to enhance the privacy and scalability of these systems. Many IDSs have been introduced, but there is a research gap concerning Host Intrusion Detection System (HIDS), which is the primary focus of our current work. Additionally, existing research predominantly focuses on the application of ML techniques and their evaluation, with limited attention to real-world implementation. We propose a lightweight HIDS that relies on the analysis of system call traces to detect malicious activities. The proposed HIDS achieved an accuracy rate of approximately 98%. Finally, by using eXplainable Artificial Intelligence methods, we sought to provide explanations for these these results.