Privacy-Preserving Intrusion Detection System for Internet of Vehicles using Split Learning

被引:0
|
作者
Agbaje, Paul [1 ]
Anjum, Afia [1 ]
Mitra, Arkajyoti [1 ]
Hounsinou, Sena [2 ]
Nwafor, Ebelechukwu [3 ]
Olufowobi, Habeeb [1 ]
机构
[1] Univ Texas Arlington, Arlington, TX 76019 USA
[2] Metro State Univ, Denver, CO USA
[3] Villanova Univ, Villanova, PA USA
来源
PROCEEDINGS OF THE IEEE/ACM 10TH INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES, BDCAT 2023 | 2023年
关键词
Intrusion detection; Split learning; Internet of Vehicles; Adaptive offloading; Optimization;
D O I
10.1145/3632366.3632388
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet of Vehicles (IoV) is envisioned to improve road safety, reduce traffic congestion, and minimize pollution. However, the connectedness of IoV entities increases the risk of cyber attacks, which can have serious consequences. Traditional intrusion detection systems (IDS) transfer large amounts of raw data to central servers, leading to potential privacy concerns. Also, training IDS on resource-constrained IoV devices generally can result in slower training times and poor service quality. To address these issues, we propose a split learning-based privacy-preserving IDS that deploys IDS on edge devices without sharing sensitive raw data. In addition, we propose a regret minimization-based adaptive offloading technique that reduces the training time on resource-constrained devices. Our approach effectively detects anomalous behavior while preserving data privacy and reducing training time, making it a practical solution for IoV. Experimental results show the effectiveness of our approach and its potential to enhance the security of the IoV network.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Using homomorphic encryption for privacy-preserving clustering of intrusion detection alerts
    Spathoulas, Georgios
    Theodoridis, Georgios
    Damiris, Georgios-Paraskevas
    INTERNATIONAL JOURNAL OF INFORMATION SECURITY, 2021, 20 (03) : 347 - 370
  • [22] Using homomorphic encryption for privacy-preserving clustering of intrusion detection alerts
    Georgios Spathoulas
    Georgios Theodoridis
    Georgios-Paraskevas Damiris
    International Journal of Information Security, 2021, 20 : 347 - 370
  • [23] Privacy-Preserving Collaborative Intrusion Detection in Edge of Internet of Things: A Robust and Efficient Deep Generative Learning Approach
    Yao, Wei
    Zhao, Hai
    Shi, Han
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (09): : 15704 - 15722
  • [24] Lightweight, Trust-Managing, and Privacy-Preserving Collaborative Intrusion Detection for Internet of Things
    Wardana, Aulia Arif
    Kolaczek, Grzegorz
    Sukarno, Parman
    APPLIED SCIENCES-BASEL, 2024, 14 (10):
  • [25] PIAS: Privacy-Preserving Incentive Announcement System Based on Blockchain for Internet of Vehicles
    Zhan, Yonghua
    Yang, Yang
    Cheng, Hongju
    Luo, Xiangyang
    Guan, Zhangshuang
    Deng, Robert H.
    IEEE TRANSACTIONS ON SERVICES COMPUTING, 2024, 17 (05) : 2762 - 2775
  • [26] Federated Learning for Privacy-Preserving Intrusion Detection in Software-Defined Networks
    Raza, Mubashar
    Jasim Saeed, Muhammad
    Riaz, Muhammad Bilal
    Awais Sattar, Muhammad
    IEEE ACCESS, 2024, 12 : 69551 - 69567
  • [27] A Distributed and Privacy-Preserving Method for Network Intrusion Detection
    Benali, Fatiha
    Bennani, Nadia
    Gianini, Gabriele
    Cimato, Stelvio
    ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2010, PT II, 2010, 6427 : 861 - +
  • [28] Quantum Split Learning for Privacy-Preserving Information Management
    Park, Soohyun
    Baek, Hankyul
    Kim, Joongheon
    PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023, 2023, : 4239 - 4243
  • [29] Privacy-Preserving Travel Time Prediction for Internet of Vehicles: A Crowdsensing and Federated Learning Approach
    Huang, Hongyu
    Sun, Cui
    Lei, Xinyu
    Mu, Nankun
    Hu, Chunqiang
    Chen, Chao
    Li, Huaqing
    Li, Yantao
    NEURAL INFORMATION PROCESSING, ICONIP 2023, PT III, 2024, 14449 : 55 - 66
  • [30] A Conditional Privacy-Preserving Identity-Authentication Scheme for Federated Learning in the Internet of Vehicles
    Xu, Shengwei
    Liu, Runsheng
    ENTROPY, 2024, 26 (07)