Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection

• URL: http://arxiv.org/abs/2506.04978v1
• Date: Thu, 05 Jun 2025 12:49:22 GMT
• Title: Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
• Authors: Gabriele Digregorio, Elisabetta Cainazzo, Stefano Longari, Michele Carminati, Stefano Zanero,
• Abstract summary: We investigate the effects of integrating Federated Learning strategies into the machine learning-based intrusion detection process for on-board vehicular networks.<n>We propose a state-of-the-art Intrusion Detection System (IDS) for Controller Area Network (CAN), based on LSTM autoencoders.
• Score: 5.9186175166428345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning (FL) solutions in the vehicular intrusion detection domain. In this paper, we investigate the effects of integrating FL strategies into the machine learning-based intrusion detection process for on-board vehicular networks. Accordingly, we propose a FL implementation of a state-of-the-art Intrusion Detection System (IDS) for Controller Area Network (CAN), based on LSTM autoencoders. We thoroughly evaluate its detection efficiency and communication overhead, comparing it to a centralized version of the same algorithm, thereby presenting it as a feasible solution.

Related papers

• CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus [45.24207460381396]
  This paper presents a novel Intrusion Detection System (IDS) designed for the Controller Area Network (CAN) environment.<n>A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks.<n>Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
  arXiv  Detail & Related papers  (2025-07-19T20:09:52Z)
• SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks [4.329477624773496]
  This paper proposes a robust SDN-based False Data Detection and Mitigation System (FDDMS) for in-vehicle networks.<n>FDDMS is designed to monitor and detect false data injection attacks in real-time.
  arXiv  Detail & Related papers  (2025-06-06T22:09:36Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Enhanced Anomaly Detection in Industrial Control Systems aided by Machine Learning [2.2457306746668766]
  This study investigates whether combining both network and process data can improve attack detection in ICSs environments. Our findings suggest that integrating network traffic with operational process data can enhance detection capabilities. Although the results are promising, they are preliminary and highlight the need for further studies.
  arXiv  Detail & Related papers  (2024-10-25T17:41:33Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Improving Transferability of Network Intrusion Detection in a Federated Learning Setup [11.98319841778396]
  Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Deep learning systems, popular due to their superior performance compared to traditional IDS, depend on availability of high quality training data for diverse intrusion classes. We propose two techniques to significantly improve the transferability of a federated intrusion detection system.
  arXiv  Detail & Related papers  (2024-01-07T17:52:41Z)
• Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning [0.0]
  Federated learning allows multiple parties to collaborate and learn a shared model without sharing their raw data. Our paper proposes a federated learning framework for intrusion detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset.
  arXiv  Detail & Related papers  (2023-11-23T04:04:20Z)
• A Method for Network Intrusion Detection Using Flow Sequence and BERT Framework [0.9208007322096533]
  This research aims to explore the possibility of using sequences of flows to improve the domain adaptation capability of network intrusion detection systems. Our proposal employs natural language processing techniques and Bidirectional Representations from Transformers framework. Early empirical results show that our approach has improved domain adaptation capability compared to previous approaches.
  arXiv  Detail & Related papers  (2023-10-26T03:56:40Z)
• Towards Scalable Wireless Federated Learning: Challenges and Solutions [40.68297639420033]
  federated learning (FL) emerges as an effective distributed machine learning framework. We discuss the challenges and solutions of achieving scalable wireless FL from the perspectives of both network design and resource orchestration.
  arXiv  Detail & Related papers  (2023-10-08T08:55:03Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [0.20236506875465865]
  Intrusion detection systems play a significant role in ensuring security and privacy of smart devices. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
  arXiv  Detail & Related papers  (2021-06-16T13:13:04Z)
• A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning [115.75967665222635]
  Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
  arXiv  Detail & Related papers  (2020-09-13T14:53:01Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.