Lightweight In-Vehicle Network Intrusion Detection Method Based on Deep Learning

doi:10.3778/j.issn.1002-8331.2206-0474

Abstract

Abstract: Existing deep learning-based in-vehicle network intrusion detection methods have problems of high computing resource consumption and delay. In order to reduce the detection delay and improve the detection effect, a lightweight in-vehicle network intrusion detection model based on visualization and improved MobileNet model is constructed, combined with transfer learning. The attack traffic is visualized as a color graph, and then the image is enlarged by bilinear interpolation to enhance the dataset and prevent model overfitting. Finally, MoblieNet is improved and the model is fine-tuned by transfer learning in order to reduce parameters and resource consumption during training. Experimental results show that the test accuracy, precision, recall and F1-score of Car-Hacking and OTIDS datasets can reach 100% on raspberry PI devices with limited computing power, and the average response time is 2.5 ms and 2.9 ms, respectively. Compared with classical deep learning models such as ResNet-18, the response time is reduced by at least 40%. Compared with detection methods such as Confidence Averaging, the consumption of training resources is reduced and the detection effect and time are ensured.

Key words: controller area network, in-vehicle network, intrusion detection, lightweight

摘要： 现有基于深度学习的车载网络入侵检测方法存在计算资源消耗和延迟较高的问题。为减少检测延迟并提高检测效果，结合迁移学习构建基于可视化和改进的MobileNet模型的轻量级车载网络入侵检测模型。将攻击流量可视化为彩色图，之后通过双线性插值方法扩大图像以增强数据集并防止模型过拟合。为减少参数和训练过程中的资源消耗，对MoblieNet进行改进，并使用迁移学习对模型进行微调。实验结果表明，该方法在算力有限的树莓派设备上对车载网络流量数据集Car-Hacking和OTIDS的测试准确率、精确率、召回率和F1值达到100%，平均响应时间分别为2.5?ms和2.9?ms，较经典的深度学习模型如ResNet-18等减少了至少40%的响应时间。相比较Confidence Averaging等检测方法，减少了训练资源的消耗，并保证了检测的效果和时间。

关键词: 控制器区域网络, 车载网络, 入侵检测, 轻量级

JIANG Yuchang, XU Yang, LI Kezi, QIN Qingkai, ZHANG Sicong. Lightweight In-Vehicle Network Intrusion Detection Method Based on Deep Learning[J]. Computer Engineering and Applications, 2023, 59(22): 284-292.

蒋玉长, 徐洋, 李克资, 秦庆凯, 张思聪. 基于深度学习的轻量级车载网络入侵检测方法[J]. 计算机工程与应用, 2023, 59(22): 284-292.

References

[1] 熊金波，毕仁万，田有亮，等.移动群智感知安全与隐私：模型、进展与趋势[J].计算机学报，2021，44（9）：1949-1966.
XIONG J B，BI R W，TIAN Y L，et al.Security and privacy in mobile crowdsensing：models，progress and trends[J].Chinese Journal of Computers，2021，44（9）：1949-1966.
[2] 姚海龙，闫巧.面向车联网增值服务的匿名认证协议的密码分析与设计[J].计算机研究与发展，2022，59（2）：440-451.
YAO H L，YAN Q.Cryptanalysis and design of anonymous authentication protocol for value-added services in Internet of vehicles[J].Journal of Computer Research and Development，2022，59（2）：440-451.
[3] 李方伟，张海波，王子心.车联网中基于MEC的V2X协同缓存和资源分配[J].通信学报，2021，42（2）：26-36.
LI F W，ZHANG H B，WANG Z X.V2X collaborative caching and resource allocation in MEC-based IoV[J].Journal on Communications，2021，42（2）：26-36.
[4] 徐川，丁颖祎，罗丽，等.车联网中基于位置服务的个性化位置隐私保护[J].软件学报，2022，33（2）：699-716.
XU C，DING YY，LUO L，et al.Personalized location privacy protection for location-based services in vehicular networks[J].Journal of Software，2022，33（2）：699-716.
[5] BUTTIGIEG R，FARRUGIA M，MELI C.Security issues in controller area networks in automobiles[C]//Proceedings of the 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering，2017：93-98.
[6] VALASEK C，MILLER C.Remote exploitation of an unaltered passenger vehicle[R].Vehicle Security Research for IOActive，2015.
[7] TALPUR A，GURUSAMY M.Machine learning for security in vehicular networks：a comprehensive survey[J].IEEE Communications Surveys & Tutorials，2022，24（1）：346-379.
[8] D’ANGELO G，CASTIGLIONE A，PALMIERI F.A cluster-based multidimensional approach for detecting attacks on connected vehicles[J].IEEE Internet of Things Journal，2020，8（16）：12518-12527.
[9] GHALEB F A，SAEED F，AL-SAREM M，et al.Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for VANET[J].Electronics，2020，9（9）：1411.
[10] TARIQ S，LEE S，WOO S S.CANTransfer：transfer learning based intrusion detection on a controller area network using convolutional LSTM network[C]//Proceedings of the 35th Annual ACM Symposium on Applied Computing，2020：1048-1055.
[11] AGRAWAL K，ALLADI T，AGRAWAL A，et al.NovelADS：a novel anomaly detection system for intra-vehicular networks[J].IEEE Transactions on Intelligent Transportation Systems，2022，23（11）：22596-22606.
[12] DESTA A K，OHIRA S，ARAI I，et al.Rec-CNN：in-vehicle networks intrusion detection using convolutional neural networks trained on recurrence plots[J].Vehicular Communications，2022，35：100470.
[13] HOWARD A，SANDLER M，CHU G，et al.Searching for MobileNetv3[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision，2019：1314-1324.
[14] SONG H M，WOO J，KIM H K.In-vehicle network intrusion detection using deep convolutional neural network[J].Vehicular Communications，2020，21：100198.
[15] LEE H，JEONG S H，KIM H K.OTIDS：a novel intrusion detection system for in-vehicle network by using remote frame[C]//Proceedings of the 2017 15th Annual Conference on Privacy，Security and Trust，2017：57-5709.
[16] LECUN Y，BOTTOU L.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE，1998，86（11）：2278-2324.
[17] GRIBBON K T，BAILEY D G.A novel approach to real-time bilinear interpolation[C]//Proceedings of the 2nd IEEE International Workshop on Electronic Design，Test and Applications，2004：126-131.
[18] HOWARD A G，ZHU M，CHEN B，et al.MobileNets：efficient convolutional neural networks for mobile vision applications[J].arXiv：1704.04861，2017.
[19] SANDLER M，HOWARD A，ZHU M，et al.MobileNetv2：inverted residuals and linear bottlenecks[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition，2018：4510-4520.
[20] 任宇杰，杨剑，刘方涛，等.基于SSD和MobileNet网络的目标检测方法的研究[J].计算机科学与探索，2019，13（11）：1881-1893.
REN Y J，YANG J，LIU F T，et al.Research on target detection method based on SSD and MobileNet network[J].Journal of Frontiers of Computer Science and Technology，2019，13（11）：1881-1893.
[21] 曾娅琴，张琳琳，张若楠，等.基于MobileNet的恶意软件家族分类模型[J].计算机工程，2020，46（4）：162-168.
ZENG Y Q，ZHANG L L，ZHANG R N，et al.Malware family classification model based on MobileNet[J].ComputerEngineering，2020，46（4）：162-168.
[22] LEONARDO M M，CARVALHO T J，REZENDE E，et al.Deep feature-based classifiers for fruit fly identification （diptera：tephritidae）[C]//Proceedings of the 2018 31st SIBGRAPI Conference on Graphics，Patterns and Images，2018：41-47.
[23] TAN M，LE Q.EfficientNet：rethinking model scaling for convolutional neural networks[C]//Proceedings of the 36th International Conference on Machine Learning，2019：6105-6114.
[24] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[25] DOSOVITSKIY A，BEYER L，KOLESNIKOV A，et al.An image is worth 16x16 words：transformers for image recognition at scale[J].arXiv：2010.11929，2020.
[26] MA N，ZHANG X，ZHENG H T，et al.ShuffleNet v2：practical guidelines for efficient CNN architecture design[C]//Proceedings of the 15th European Conference on Computer Vision，2018：116-131.
[27] MOUBAYED A，SHAMI A，HEIDARI P，et al.Edge-enabled V2X service placement for intelligent transportation systems[J].IEEE Transactions on Mobile Computing，2020，20（4）：1380-1392.
[28] YANG L，SHAMI A.A transfer learning and optimized CNN based intrusion detection system for Internet of vehicles[J].arXiv：2201.11812，2022.
[29] JAVED A R，UR REHMAN S，KHAN M U，et al.CAN-
intelliIDS：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.