A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

• URL: http://arxiv.org/abs/2201.11812v1
• Date: Thu, 27 Jan 2022 21:24:09 GMT
• Title: A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
• Authors: Li Yang and Abdallah Shami
• Abstract summary: 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.
• Score: 10.350337750192997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

Related papers

• AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN) [4.081467217340597]
  The Controller Area Network (CAN) bus is a central system for managing in-vehicle communication between the electronic control units (ECUs) CAN protocol poses security challenges due to inherent vulnerabilities, lacking encryption and authentication, which, combined with an expanding attack surface, necessitates robust security measures. This paper considers the latest ROAD dataset, containing stealthy and sophisticated injections.
  arXiv  Detail & Related papers  (2024-08-30T12:26:23Z)
• Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning [44.17644657738893]
  This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL) to optimize AoI across the system.
  arXiv  Detail & Related papers  (2024-07-01T15:37:38Z)
• Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
  Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks. In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
  arXiv  Detail & Related papers  (2024-03-29T12:01:31Z)
• Cyber-Twin: Digital Twin-boosted Autonomous Attack Detection for Vehicular Ad-Hoc Networks [8.07947129445779]
  The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS) VANETs are increasingly susceptible to cyberattacks, such as jamming and distributed denial of service (DDoS) attacks. Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity. This study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs.
  arXiv  Detail & Related papers  (2024-01-25T08:05:41Z)
• 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)
• 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)
• 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)
• MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles [12.280524044112708]
  A hybrid intrusion detection system (IDS) is proposed to detect both known and unknown attacks on vehicular networks. The proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms.
  arXiv  Detail & Related papers  (2021-05-26T17:36:35Z)
• Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks [2.141079906482723]
  Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. No previous studies have focused on intrusion detection in automotive Ethernet-based networks. We present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks.
  arXiv  Detail & Related papers  (2021-02-06T09:37:09Z)

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.