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
  • [41] Wildland Fire Spread Modeling Using Convolutional Neural Networks
    Hodges, Jonathan L.
    Lattimer, Brian Y.
    FIRE TECHNOLOGY, 2019, 55 (06) : 2115 - 2142
  • [42] Using probabilistic neural networks for modeling metal fatigue and random vibration in process pipework
    Nashed, Mohamad Shadi
    Mohamed, M. Shadi
    Shady, Omar Tawfik
    Renno, Jamil
    FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES, 2022, 45 (04) : 1227 - 1242
  • [43] The forward EEG solutions can be computed using artificial neural networks
    Sun, MG
    Sclabassi, RJ
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2000, 47 (08) : 1044 - 1050
  • [44] MODELING PHASE-CHANGE MATERIALS HEAT CAPACITY USING ARTIFICIAL NEURAL NETWORKS
    Delcroix, B.
    Kummert, M.
    Daoud, A.
    HEAT TRANSFER RESEARCH, 2018, 49 (07) : 617 - 631
  • [45] Enhancing Security in IoT-Assisted UAV Networks Using Adaptive Mongoose Optimization Algorithm With Deep Learning
    Alotaibi, Saud S.
    Sayed, Ahmed
    Abd Elhameed, Elmouez Samir
    Alghushairy, Omar
    Assiri, Mohammed
    Ibrahim, Sara Saadeldeen
    IEEE ACCESS, 2024, 12 : 63768 - 63776
  • [46] Enhancing Performance of Multi-Input Neural Networks Using Hadamard Product
    Won-Joong, Kim
    Inwoo, Kim
    Minsoo, Lee
    Soo-Hong, Lee
    2023 IEEE INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING, ICDL, 2023, : 139 - 143
  • [47] Identification of wear and misalignment on journal bearings using artificial neural networks
    Saridakis, K. M.
    Nikolakopoulos, P. G.
    Papadopoulos, C. A.
    Dentsoras, A. J.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY, 2012, 226 (J1) : 46 - 56
  • [48] Identification of organic compounds using artificial neural networks and refractive index
    Kirigiti, Innocent Abel
    Aminah, Nanik Siti
    Thomas, Samson
    JOURNAL OF THE SERBIAN CHEMICAL SOCIETY, 2023, 88 (10) : 1013 - 1023
  • [49] Network Topology Identification using Supervised Pattern Recognition Neural Networks
    Perumalla, Aniruddha
    Koru, Ahmet Taha
    Johnson, Eric Norman
    ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 1, 2021, : 258 - 264
  • [50] Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks
    Garcia-Avila, Josue
    Torres Serrato, Diego de Jesus
    Rodriguez, Ciro A.
    Vargas Martinez, Adriana
    Ramirez Cedillo, Erick
    Israel Martinez-Lopez, J.
    POLYMERS, 2022, 14 (23)