UGL: A comprehensive hybrid model integrating GCN and LSTM for enhanced intrusion detection in UAV controller area networks

Abstract

The Unmanned Aerial Vehicle Controller Area Network (UAVCAN) is a lightweight communication protocol based on the Controller Area Network (CAN) bus, designed to facilitate communication among various components within unmanned aerial vehicles (UAVs). Traditional CAN-based intrusion detection and anomaly monitoring methods primarily target vehicle networks, rendering them less adaptable and effective for UAV systems due to differences in network structure and data patterns. UAV networks encounter significant challenges, including limited information density and a reduced number of electronic components. To address these challenges, this paper introduces two key innovations to enhance security in UAV networks. First, Based on the extended dataset, we propose a novel graph construction method specifically designed for scenarios where UAVs have only a few Electronic Control Unit (ECU) nodes, effectively enhancing the information density. Secondly, this study designs an innovative network attack detection model called UAV-GCNLSTM (UGL), which combines the efficiency of Graph Convolutional Networks (GCN) in capturing network topology with the capability of Long Short-Term Memory networks (LSTM) in processing sequential data. Experimental results demonstrate that the UGL model achieves an accuracy of 1.0000 for Flooding attacks, 0.9854 for Fuzzy attacks, and 0.9635 for Replay attacks, significantly outperforming the compared models.