Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort

TL;DR

This paper introduces an unsupervised intrusion detection system for UAVs that reduces labeling effort and can detect unknown attacks by analyzing reconstruction loss from an autoencoder trained on benign flight data.

Contribution

The study presents a novel unsupervised learning approach using autoencoders for UAV intrusion detection, eliminating the need for attack data labeling and enabling detection of unknown threats.

Findings

Autoencoder trained on benign data detects attacks via higher reconstruction loss.

Model effectively distinguishes between normal and attacked UAV flights.

Proposed method reduces labeling effort and improves real-world applicability.

Abstract

Along with the importance of safety, an IDS has become a significant task in the real world. Prior studies proposed various intrusion detection models for the UAV. Past rule-based approaches provided a concrete baseline IDS model, and the machine learning-based method achieved a precise intrusion detection performance on the UAV with supervised learning models. However, previous methods have room for improvement to be implemented in the real world. Prior methods required a large labeling effort on the dataset, and the model could not identify attacks that were not trained before. To jump over these hurdles, we propose an IDS with unsupervised learning. As unsupervised learning does not require labeling, our model let the practitioner not to label every type of attack from the flight data. Moreover, the model can identify an abnormal status of the UAV regardless of the type of attack. We trained an autoencoder with the benign flight data only and checked the model provides a different reconstruction loss at the benign flight and the flight under attack. We discovered that the model produces much higher reconstruction loss with the flight under attack than the benign flight; thus, this reconstruction loss can be utilized to recognize an intrusion to the UAV. With consideration of the computation overhead and the detection performance in the wild, we expect our model can be a concrete and practical baseline IDS on the UAV.