# Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles

arXiv: https://arxiv.org/abs/2412.02539 · HTML: https://mlsanity.com/paper/2412.02539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures](https://mlsanity.com/paper/2205.04711/) (2022)
- [Low-latency Mini-batch GNN Inference on CPU-FPGA Heterogeneous Platform](https://mlsanity.com/paper/2206.08536/) (2022)
- [Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack](https://mlsanity.com/paper/2108.01125/) (2021)
- [Graphite: A Graph-based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation](https://mlsanity.com/paper/2407.20462/) (2024)
- [Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection](https://mlsanity.com/paper/2110.07467/) (2021)
- [GCNIDS: Graph Convolutional Network-Based Intrusion Detection System for CAN Bus](https://mlsanity.com/paper/2309.10173/) (2023)
- [LEAP: A Lightweight Encryption and Authentication Protocol for In-Vehicle Communications](https://mlsanity.com/paper/1909.10380/) (2019)
- [Security of Connected and Automated Vehicles](https://mlsanity.com/paper/2012.13464/) (2020)
- [WedgeTail: An Intrusion Prevention System for the Data Plane of Software Defined Networks](https://mlsanity.com/paper/1708.05477/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks](https://mlsanity.com/paper/2510.02711/) (2025)

## Abstract (arXiv)

The network of services, including delivery, farming, and environmental monitoring, has experienced exponential expansion in the past decade with Unmanned Aerial Vehicles (UAVs). Yet, UAVs are not robust enough against cyberattacks, especially on the Controller Area Network (CAN) bus. The CAN bus is a general-purpose vehicle-bus standard to enable microcontrollers and in-vehicle computers to interact, primarily connecting different Electronic Control Units (ECUs). In this study, we focus on solving some of the most critical security weaknesses in UAVs by developing a novel graph-based intrusion detection system (IDS) leveraging the Uncomplicated Application-level Vehicular Communication and Networking (UAVCAN) protocol. First, we decode CAN messages based on UAVCAN protocol specification; second, we present a comprehensive method of transforming tabular UAVCAN messages into graph structures. Lastly, we apply various graph-based machine learning models for detecting cyber-attacks on the CAN bus, including graph convolutional neural networks (GCNNs), graph attention networks (GATs), Graph Sample and Aggregate Networks (GraphSAGE), and graph structure-based transformers. Our findings show that inductive models such as GATs, GraphSAGE, and graph-based transformers can achieve competitive and even better accuracy than transductive models like GCNNs in detecting various types of intrusions, with minimum information on protocol specification, thus providing a generic robust solution for CAN bus security for the UAVs. We also compared our results with baseline single-layer Long Short-Term Memory (LSTM) and found that all our graph-based models perform better without using any decoded features based on the UAVCAN protocol, highlighting higher detection performance with protocol-independent capability.