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
  • [21] CONSTRUCTION OF CUSTOMIZABLE SOA SECURITY FRAMEWORK USING ARTIFICIAL NEURAL NETWORKS
    Ibrahim, Mohamed B.
    Hassan, Mohd Fadzil
    JURNAL TEKNOLOGI, 2016, 78 (12-3): : 69 - 75
  • [22] Monitoring system security using neural networks and support vector machines
    Mukkamala, S
    Janoski, G
    Sung, A
    HYBRID INFORMATION SYSTEMS, 2002, : 121 - 137
  • [23] An Overview of using of Artificial Intelligence in Enhancing Security and Privacy in Mobile Social Networks
    Fakhouri, Hussam N.
    Alawadi, Sadi
    Awaysheh, Feras M.
    Hamad, Faten
    Alzubi, Sawsan
    AlAdwan, Mohammad Naser
    2023 EIGHTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING, FMEC, 2023, : 42 - 51
  • [24] In Silico Modeling of Pharmaceutical Formulation using Artificial Neural Networks
    Piriyaprasarth, S.
    Patomchaiviwat, V.
    Sriamonsak, P.
    2009 INTERNATIONAL CONFERENCE ON BIOMEDICAL AND PHARMACEUTICAL ENGINEERING, 2009, : 154 - 158
  • [25] Fatigue Characterization of WMA and Modeling Using Artificial Neural Networks
    Abd, Duraid M.
    Al-Khalid, Hussain
    JOURNAL OF MATERIALS IN CIVIL ENGINEERING, 2022, 34 (03)
  • [26] Identification of probe request attacks in WLANs using neural networks
    Ratnayake, Deepthi N.
    Kazemian, Hassan B.
    Yusuf, Syed A.
    NEURAL COMPUTING & APPLICATIONS, 2014, 25 (01) : 1 - 14
  • [27] Wireless Technology Identification Using Deep Convolutional Neural Networks
    Bitar, Naim
    Muhammad, Siraj
    Refai, Hazem H.
    2017 IEEE 28TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2017,
  • [28] Wireless Channel Scenario Identification Using Convolutional Neural Networks
    Gopal, Govind R.
    Chen, Jie
    Hillery, William J.
    Tan, Jun
    Ozen, Serdar
    Zhu, Qiping
    2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING, 2023,
  • [29] Identification of Plant Nutrient Deficiencies Using Convolutional Neural Networks
    Watchareeruetai, Ukrit
    Noinongyao, Pavit
    Wattanapaiboonsuk, Chaiwat
    Khantiviriya, Puriwat
    Duangsrisai, Sutsawat
    2018 6TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON), 2018,
  • [30] Source Code Authorship Identification Using Deep Neural Networks
    Kurtukova, Anna
    Romanov, Aleksandr
    Shelupanov, Alexander
    SYMMETRY-BASEL, 2020, 12 (12): : 1 - 17