Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks

• URL: http://arxiv.org/abs/2306.08566v1
• Date: Wed, 14 Jun 2023 15:17:58 GMT
• Title: Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks
• Authors: Zhe Wang, Tingkai Yan
• Abstract summary: This paper proposes a Federated Learning-based Vehicle Trajectory Prediction Algorithm against Cyberattacks. The proposed FL-TP algorithm can improve cyberattack detection and trajectory prediction by up to 6.99% and 54.86%, respectively.
• Score: 4.0989155767548375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of the Internet of Vehicles (IoV), vehicle wireless communication poses serious cybersecurity challenges. Faulty information, such as fake vehicle positions and speeds sent by surrounding vehicles, could cause vehicle collisions, traffic jams, and even casualties. Additionally, private vehicle data leakages, such as vehicle trajectory and user account information, may damage user property and security. Therefore, achieving a cyberattack-defense scheme in the IoV system with faulty data saturation is necessary. This paper proposes a Federated Learning-based Vehicle Trajectory Prediction Algorithm against Cyberattacks (FL-TP) to address the above problems. The FL-TP is intensively trained and tested using a publicly available Vehicular Reference Misbehavior (VeReMi) dataset with five types of cyberattacks: constant, constant offset, random, random offset, and eventual stop. The results show that the proposed FL-TP algorithm can improve cyberattack detection and trajectory prediction by up to 6.99% and 54.86%, respectively, under the maximum cyberattack permeability scenarios compared with benchmark methods.

Related papers

• Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data [57.22545280370174]
  On Path Diagnostic - Intrusion & Inference (OPD-II) is a novel path inference attack leveraging a physical car model and a map matching algorithm. We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios.
  arXiv  Detail & Related papers  (2024-06-30T04:21:46Z)
• SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic [49.549771439609046]
  We propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication. Extensive experimental results show the effectiveness and efficiency of SISSA.
  arXiv  Detail & Related papers  (2024-02-21T03:31:40Z)
• A V2X-based Privacy Preserving Federated Measuring and Learning System [0.0]
  We propose a federated measurement and learning system that provides real-time data to fellow vehicles over Vehicle-to-Vehicle (V2V) communication. We also operate a federated learning scheme over the Vehicle-to-Network (V2N) link to create a predictive model of the transportation network. Results indicate that the proposed FL scheme improves learning performance and prevents eavesdropping at the aggregator server side.
  arXiv  Detail & Related papers  (2024-01-24T23:11:11Z)
• Detecting stealthy cyberattacks on adaptive cruise control vehicles: A machine learning approach [5.036807309572884]
  More insidious attacks, which only slightly alter driving behavior, can result in network-wide increases in congestion, fuel consumption, and even crash risk without being easily detected. We present a traffic model framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, false data injection attacks on sensor measurements, and denial-of-service (DoS) attacks. A novel generative adversarial network (GAN)-based anomaly detection model is proposed for real-time identification of such attacks using vehicle trajectory data.
  arXiv  Detail & Related papers  (2023-10-26T01:22:10Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
  Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors. We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories. Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
  arXiv  Detail & Related papers  (2022-09-19T03:34:59Z)
• LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles [7.795462813462946]
  Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed. We propose a novel ensemble IDS framework named Leader Class and Confidence Decision Ensemble (LCCDE) LCCDE is constructed by determining the best-performing ML model among three advanced algorithms.
  arXiv  Detail & Related papers  (2022-08-05T22:30:34Z)
• 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)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles [10.350337750192997]
  In this paper, a transfer learning and ensemble learning-based IDS is proposed for Internet of Vehicles (IoV) systems. The proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two public benchmark IoV security datasets. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.
  arXiv  Detail & Related papers  (2022-01-27T21:24:09Z)

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.