Machine learning approaches to intrusion detection in unmanned aerial vehicles (UAVs)

Abstract

Unmanned Aerial Vehicles (UAVs) have been gaining popularity in various commercial, civilian, and military applications due to their efficiency and cost-effectiveness. However, the increasing demand for UAVs makes them vulnerable to various cyberattacks/intrusions that could have devastating consequences at an individual, organizational, and national level. To mitigate this, prompt detection of such threats is crucial in order to prevent potential damage and ensure safe and secure operations. In this work, we provide an overview of UAV systems’ architecture, security, and privacy requirements. We then analyze potential threats to UAVs, providing an evaluation of countermeasures for UAV-based attacks. We also present a comprehensive and timely exploration of state-of-the-art UAV Intrusion Detection Systems (IDSs), specifically focusing on Machine Learning (ML)-based approaches. We look at the increasing importance of using ML for detecting intrusions in UAVs, which have gained significant attention from both academia and industry. This study also takes a step forward by pointing out and classifying contemporary IDSs based on their detection methods, feature selection techniques, evaluation datasets, and performance metrics. By evaluating existing research, we aim to provide more insight into the issues and limitations of current UAV IDSs. Additionally, we identify research gaps and challenges while suggesting potential future research directions in this domain.