Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (3): 857-865.doi: 10.13229/j.cnki.jdxbgxb.20240044

Previous Articles     Next Articles

In⁃vehicle network intrusion detection system based on CAN bus data

Yin-fei DAI(),Xiu-zhen ZHOU,Yu-bao LIU(),Zhi-yuan LIU   

  1. School of Computer Science and Technology，Changchun University，Changchun 130022，China
  • Received:2024-01-13 Online:2025-03-01 Published:2025-05-20
  • Contact: Yu-bao LIU E-mail:daiyf@ccu.edu.cn;1574869421@qq.com

RICH HTML

 2   

PDF (PC)

163

Abstract:

A novel in-vehicle network intrusion detection method based on controllerarea network （CAN） bus data was proposed and named as IncepNet method. First， a real Car-hacking dataset was selected for data preprocessing. Image processing methods were used to reorganize the time-series data in this dataset according to certain rules， and converted it into image data suitable for use as the input of convolutional neural network. Next， the existing residual network （Inception-ResNet） was optimized and followed by the addition of a long short-term memory （LSTM） layer with multi-batch normalization and a Dropout layer. Finally， a confusion matrix including recognition rate， accuracy， F1-score （F1-score）， and number of false alarms （FAR） was used to demonstrate the superior accuracy and reliability of the model. The results show that the model has low false alarm rate， high detection accuracy and high detection rate， and its efficiency is significantly better than previous detection methods based on other machine learning.

Key words: computer technology, telematics, intrusion detection, machine learning, neural networks, long short-term memory

CLC Number: 

  • TP393

Fig.1

Bit signaling logic for CAN buses"

Fig.2

CAN bus's structure"

Fig.3

Standard format CAN and extended format CAN"

Fig.4

DCNN network structure"

Fig.5

Structure of LSTM cell"

Fig.6

Data processing procedure"

Fig.7

Samples for each category"

Fig.8

CAN intrusion detection architecture"

Fig.9

Network infrastructure"

Table 1

IncepNet model structure"

神经网络模块化结构参数
Inception层Input224×224×3
Modified_A13×13×128
Modified_B6×6×448
Modified_C2×2×896
LSTM层GlobalAveragePool2D896
Denseunits=896， activation='relu'
LSTMLSTM_units=128
分类层Dropoutp=0.5，inplace=False
Softmaxnum_class=5

Fig.10

Confusion matrix obtained using IncepNet model"

Fig.11

Confusion matrix obtained using DCNN model"

Fig.12

Confusion matrix obtained using LSTM model"

Fig.13

Evaluation data obtained after training the IncepNet model"

Table 2

Model evaluation"

模型评价指标
AccuracyPrecisionRecallF1
IncepNet99.9899.9999.9699.98
DCNN99.9599.9699.9299.94
LSTM99.8699.9199.7499.83
DCNN-LSTM99.2996.6993.8295.19

Fig.14

Accuracy of model training based on five types of data"

Table 3

Comparison results of different models"

模型数据
NormalDoSgearRPMFuzzy
IncepNet99.5999.8699.3891.1487.70
DCNN99.9099.8899.1198.5682.77
LSTM99.8099.7098.9096.9676.74
DCNN-LSTM99.4999.9198.5889.6780.47
1 Miller C, Valasek C. Remote exploitation of an unaltered passenger vehicle[J]. Black Hat USA, 2015, 2015(S91): 1-91.
2 Javed A R, Ur Rehman S, Khan M U, et al. CANintelliIDS: detecting in-vehicle intrusion attacks on a controller area network using CNN and attention-based GRU[J]. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1456-1466.
3 Kim S H, Seo S H, Kim J H, et al. A gateway system for an automotive system: LIN, CAN, and FlexRay[C]∥6th IEEE International Conference on Industrial Informatics, Foshan, China, 2008: 967-972.
4 Farsi M, Ratcliff K, Barbosa M. An overview of controller area network[J]. Computing & Control Engineering Journal, 1999, 10(3): 113-120.
5 朱鹏程, 涂江健, 招志才, 等. 车联网威胁分析和入侵检测关键技术[J]. 自动化博览, 2022,39(1):88-93.
Zhu Peng-cheng, Tu Jiang-jian, Zhao Zhi-cai, et al. Key technologies for threat analysis and intrusion detection in telematics[J]. Automation Expo, 2022, 39(1): 88-93.
6 Checkoway S, McCoy D, Kantor B, et al. Comprehensive experimental analyses of automotive attack surfaces[C]∥20th USENIX Security Symposium,San Francisco,USA, 2011: 99-114.
7 Xu Z, Wang Z. Feature selection-based intrusion detection system for internet of vehicles[J]. IEEE Transactions on Intelligent Transportation Systems,2021,22(1): 1-11.
8 Song Y, Du J, Chen X, et al. Anomaly detection algorithm based on improved one-class support vector machine[J]. Journal of Ambient Intelligence and Humanized Computing,2019, 10(1): 219-228.
9 Wu S, Xu J, Wang Y,et al. Anomaly detection algorithm based on improved spectral clustering[J]. Journal of Ambient Intelligence and Humanized Computing,2022, 13(1): 513-521.
10 丁宝星. 基于人工智能的网络入侵检测与防御研究[J]. 中国信息化, 2023(11): 76-78.
Ding Bao-xing. Research on network intrusion detection and defense based on artificial intelligence[J]. China Informatization, 2023(11):76-78.
11 陈力, 王海江, 吴凯. 基于卷积神经网络的车载网络入侵检测方法[J].浙江科技学院学报, 2021, 33(1):59-66, 84.
Chen Li, Wang Hai-jiang, Wu Kai. Intrusion detection method for in-vehicle networks based on convolutional neural networks[J]. Journal of Zhejiang Institute of Science and Technology, 2021, 33(1):59-66, 84.
12 Zhang J, Wu Z C, Li F, et al. A deep learning framework for driving behavior identification on in-vehicle CAN-BUS sensor data[J]. Sensors, 2019, 19(6): 1356.
13 Chung J, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J/OL]. [2024-01-05].
14 关宇昕, 冀浩杰, 崔哲, 等. 智能网联汽车车载CAN网络入侵检测方法综述[J]. 汽车工程, 2023,45(6): 922-935.
Guan Yu-xin, Ji Hao-jie, Cui Zhe, et al. A review of intrusion detection methods for in-vehicle CAN networks in smart connected vehicles[J]. Automotive Engineering,2023, 45(6): 922-935.
15 Sun H, Chen M, Weng J, et al. Anomaly detection for in-vehicle network using CNN-LSTM with attention mechanism[J]. IEEE Transactions on Vehicular Technology, 2021, 70(10): 10880-10893.
16 Seo E, Song H M, Kim H K. GIDS: GAN based intrusion detection system for in-vehicle network[C]∥16th Annual Conference on Privacy, Security and Trust (PST),Waterloo, Canada,2018: 1-6.
17 Ergen T, Kozat S S. Unsupervised anomaly detection with LSTM neural networks[J]. IEEE transactions on Neural Networks and Learning Systems, 2019, 31(8): 3127-3141.
18 Song H M, Kim H K. Can network intrusion datasets[EB/OL].[2024-01-06].
[1] Hua-zhen FANG,Li LIU,Qing GU,Xiao-feng XIAO,Yu MENG. Driving intention recognition based on trajectory prediction and extreme gradient boosting [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 623-630.
[2] Xiao-dong CAI,Qing-song ZHOU,Yan-yan ZHANG,Yun XUE. Social recommendation based on global capture of dynamic， static and relational features [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 700-708.
[3] Hao WANG,Bin ZHAO,Guo-hua LIU. Temporal and motion enhancement for video action recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 339-346.
[4] Lei ZHANG,Jing JIAO,Bo-xin LI,Yan-jie ZHOU. Large capacity semi structured data extraction algorithm combining machine learning and deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2631-2637.
[5] Ping YU,Kang ZHAO,Jie CAO. Rolling bearing fault diagnosis based on optimized A-BiLSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2156-2166.
[6] Cheng CHEN,Pei-xin SHI,Peng-jiao JIA,Man-man DONG. Correlation analysis of shield driving parameters and structural deformation prediction based on MK-LSTM algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(6): 1624-1633.
[7] Qiu-zhan ZHOU,Ze-yu JI,Cong WANG,Jing RONG. Non-intrusive load monitoring via online compression and reconstruction [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(6): 1796-1806.
[8] Ling HUANG,Zuan CUI,Feng YOU,Pei-xin HONG,Hao-chuan ZHONG,Yi-xuan ZENG. Vehicle trajectory prediction model for multi-vehicle interaction scenario [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1188-1195.
[9] Shi-feng NIU,Shi-jie YU,Yan-jun LIU,Chong MA. Real-time detection method of angry driving behavior based on bracelet data [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3505-3512.
[10] Li-zhao DAI,Chong WANG,Ping YUAN,Lei WANG. Prediction model for shear capacity of corroded RC beams based on interpretable machine learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3231-3243.
[11] Xian-feng REN,Wen-wen YUAN,Xue-qiang WU,Yan-ru SHI,Meng-meng YAO,Kai-xuan ZHANG,Rui-xin YANG,Yue PAN. Multi-dimensional aging diagnosis of lithium-ion battery with a long short-term memory neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3135-3147.
[12] Zhong-liang FU,Xiao-qing CHEN,Wei REN,Yu YAO. Random K-nearest neighbor algorithm with learning process [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(1): 209-220.
[13] Qing-tian GENG,Zhi LIU,Qing-liang LI,Fan-hua YU,Xiao-ning LI. Prediction of soil moisture based on a deep learning model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2430-2436.
[14] Shuang LI,Zi-rui LIN,Song YE,Xu LIU,Ji-song ZHAO. Orbital capability evaluation and trajectory reconstruction for launch vehicle with thrust decline [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2245-2253.
[15] Rong-han YAO,Wen-tao XU,Wei-wei GUO. Drivers' takeover behavior and intention recognition based on factor and long short⁃term memory [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 758-771.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd