Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach

TL;DR

This paper introduces a novel autoencoder-based feature extraction and machine learning approach for intrusion detection in UAV communications, utilizing real-world datasets to improve detection accuracy and classification of attacks.

Contribution

It is the first to propose an autoencoder-based intrusion detection method for UAVs using actual datasets, outperforming existing feature selection methods.

Findings

Outperforms baseline methods in detection accuracy

Effective feature extraction with autoencoder improves classification

Works well on real UAV intrusion datasets

Abstract

This paper proposes a novel intrusion detection method for unmanned aerial vehicles (UAV) in the presence of recent actual UAV intrusion dataset. In particular, in the first stage of our method, we design an autoencoder architecture for effectively extracting important features, which are then fed into various machine learning models in the second stage for detecting and classifying attack types. To the best of our knowledge, this is the first attempt to propose such the autoencoder-based machine learning intrusion detection method for UAVs using actual dataset, while most of existing works only consider either simulated datasets or datasets irrelevant to UAV communications. Our experiment results show that the proposed method outperforms the baselines such as feature selection schemes in both binary and multi-class classification tasks.