Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks

Zhang, Hengrun; Zeng, Kai; Lin, Shuai

doi:10.1109/tifs.2023.3240291

Cited by 35 publications

(8 citation statements)

References 48 publications

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“…Existing IDSs are basically divided into neural network methods [14,[21][22][23][24][25][26][27][28][29][30], traditional machine learning methods [31][32][33][34][35], and other efficient statistics methods that utilize the characteristics of the CAN bus [10,13,26,[36][37][38][39][40][41][42][43][44][45][46]. In the methods related to neural networks, as in Refs.…”

Section: Related Workmentioning

confidence: 99%

Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus

Fang,

Zhang,

et al. 2024

Applied Sciences

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The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms.

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Section: Related Workmentioning

confidence: 99%

Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus

Fang,

Zhang,

et al. 2024

Applied Sciences

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“…b) Graph embedding problem in distributed graph data: As the local graph topology changes over time or each node contains time-series data, it is necessary to embed the spatial-temporal (ST) information for the distributed graph data. STFL [65], Feddy [66], and 4D-FED-GNN+ [67], [68] deal with ST graph data embedding differently. STFL [65], [68] does not consider the temporal relationship in the graph embedding.…”

Section: A Horizontal Fedgnnsmentioning

confidence: 99%

Federated Graph Neural Networks: Overview, Techniques, and Challenges

Liu,

Xing,

Deng

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Graph neural networks (GNNs) have attracted extensive research attention in recent years due to their capability to progress with graph data and have been widely used in practical applications. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in federated learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated GNNs (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this article, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-D taxonomy of the FedGNN literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNN training and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.

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“…The second, by Tax and Duin, restricts training data to a hypersphere [28]. One-class classification problems commonly exist in novelty and outlier detection [29][30][31][32]. Successful application of one-class classifiers depends on whether the feature space is well constructed.…”

Section: One-class Classification Problemsmentioning

confidence: 99%

Detecting Generated Images by Real Images

Liu¹,

Yang²,

Bi³

et al. 2022

Lecture Notes in Computer Science

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As deep learning technology continues to evolve, the images yielded by generative models are becoming more and more realistic, triggering people to question the authenticity of images. Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This learning paradigm will result in efficiency and generalization issues, making detection methods always lag behind generation methods. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace. As a result, images from different generative models can be detected, solving some long-existing problems in the field. Experimental results show that although our method was trained only by real images and uses 99.9% less training data than other deep learning-based methods, it can compete with state-of-the-art methods and shows excellent performance in detecting emerging generative models with high inference efficiency. Moreover, the proposed method shows robustness against various post-processing. These advantages allow the method to be used in real-world scenarios.

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Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks

Cited by 35 publications

References 48 publications

Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus

Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus

Federated Graph Neural Networks: Overview, Techniques, and Challenges

Detecting Generated Images by Real Images

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