Enhancing ECU identification security in CAN networks using distortion modeling and neural networks

被引:1
|
作者
Hafeez, Azeem [1 ]
Malik, Hafiz [1 ]
Irtaza, Aun [1 ]
Uddin, Md Zia [2 ]
Noori, Farzan M. [3 ]
机构
[1] Univ Michigan Dearborn, Dept Elect & Comp Engn, Dearborn, MI USA
[2] SINTEF Digital, Dept Sustainable Commun Technol, Oslo, Norway
[3] Univ Oslo, Dept Informat, Oslo, Norway
来源
FRONTIERS IN COMPUTER SCIENCE | 2024年 / 6卷
基金
美国国家科学基金会;
关键词
intrusion detection system; electronic control unit (ECU); controller area network (CAN); machine learning; artificial neural network (ANN); digital-to-analog converter (DAC); performance matrix (PM); SOLAR GRADE SILICON; INTRUSION DETECTION; CONTROLLER; CHALLENGES; VEHICLES; BEHAVIOR;
D O I
10.3389/fcomp.2024.1392119
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
A novel technique for electronic control unit (ECU) identification is proposed in this study to address security vulnerabilities of the controller area network (CAN) protocol. The reliable ECU identification has the potential to prevent spoofing attacks launched over the CAN due to the lack of message authentication. In this regard, we model the ECU-specific random distortion caused by the imperfections in the digital-to-analog converter and semiconductor impurities in the transmitting ECU for fingerprinting. Afterward, a 4-layered artificial neural network (ANN) is trained on the feature set to identify the transmitting ECU and the corresponding ECU pin. The ECU-pin identification is also a novel contribution of this study and can be used to prevent voltage-based attacks. We have evaluated our method using ANNs over a dataset generated from 7 ECUs with 6 pins, each having 185 records, and 40 records for each pin. The performance evaluation against state-of-the-art methods revealed that the proposed method achieved 99.4% accuracy for ECU identification and 96.7% accuracy for pin identification, which signifies the reliability of the proposed approach.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Modeling and identification of flexible link using neural networks
    Rao, SR
    Bandyopadhyay, B
    Seth, B
    PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY 2000, VOLS 1 AND 2, 2000, : 237 - 242
  • [2] Real-Time Security Warning and ECU Identification for In-Vehicle Networks
    Wei, Hongqian
    Ai, Qiang
    Zhao, Wenqiang
    Zhang, Youtong
    IEEE SENSORS JOURNAL, 2023, 23 (17) : 20258 - 20266
  • [3] Enhancing security selection in the Australian stockmarket using fundamental analysis and neural networks
    Vanstone, B
    Finnie, G
    Tan, C
    PROCEEDINGS OF THE EIGHTH IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, 2004, : 305 - 310
  • [4] Modeling of Passive Intermodulation Distortion Using the Neural Networks and the Cubic Volterra Filter
    Jang, Beomhee
    Im, Sungbin
    Kim, Chonghoon
    Hong, Seungmo
    2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE, 2019, : 1042 - 1046
  • [5] SYSTEM-IDENTIFICATION USING ARMA MODELING AND NEURAL NETWORKS
    PRAMOD, BR
    BOSE, SC
    JOURNAL OF ENGINEERING FOR INDUSTRY-TRANSACTIONS OF THE ASME, 1993, 115 (04): : 487 - 491
  • [6] Modeling and identification of fertility maps using artificial neural networks
    Ulson, JAC
    da Silva, IN
    Benez, SH
    Boas, RLV
    SMC 2000 CONFERENCE PROCEEDINGS: 2000 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOL 1-5, 2000, : 2673 - 2678
  • [7] A general approach for hysteresis modeling and identification using neural networks
    Beuschel, M
    Hangl, F
    Schroder, D
    IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, 1998, : 2425 - 2428
  • [8] ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks
    Hafeez, Azeem
    Topolovec, Kenneth
    Awad, Selim
    2019 15TH INTERNATIONAL COMPUTER ENGINEERING CONFERENCE (ICENCO 2019), 2019, : 29 - 38
  • [9] Security software using neural networks
    Zimmer, JP
    Miteran, J
    Yang, F
    Paindavoine, M
    IECON '98 - PROCEEDINGS OF THE 24TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, 1998, : 72 - 74
  • [10] Adding Security to Networks-on-Chip using Neural Networks
    Madden, Kyle
    Harkin, Jim
    McDaid, Liam
    Nugent, Chris
    2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2018, : 1299 - 1306