Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI
Pages 93 - 103
Abstract
In the evolving landscape of autonomous vehicles, ensuring robust in-vehicle network (IVN) security is paramount. This paper introduces an advanced intrusion detection system (IDS) called KD-XVAE that uses a Variational Autoencoder (VAE)-based knowledge distillation approach to enhance both performance and efficiency. Our model significantly reduces complexity, operating with just 1669 parameters and achieving an inference time of 0.3 ms per batch, making it highly suitable for resource-constrained automotive environments. Evaluations in the HCRL Car-Hacking dataset demonstrate exceptional capabilities, attaining perfect scores (Recall, Precision, F1 Score of 100%, and FNR of 0%) under multiple attack types, including DoS, Fuzzing, Gear Spoofing, and RPM Spoofing. Comparative analysis on the CICIoV2024 dataset further underscores its superiority over traditional machine learning models, achieving perfect detection metrics. We furthermore integrate Explainable AI (XAI) techniques to ensure transparency in the model's decisions. The VAE compresses the original feature space into a latent space, on which the distilled model is trained. SHAP (SHapley Additive exPlanations) values provide insights into the importance of each latent dimension, mapped back to original features for intuitive understanding. Our paper advances the field by integrating state-of-the-art techniques, addressing critical challenges in the deployment of efficient, trustworthy, and reliable IDSes for autonomous vehicles, ensuring enhanced protection against emerging cyber threats.
References
[1]
A. Altalbe. 2023. Enhanced intrusion detection in in-vehicle networks using advanced feature fusion and stacking-enriched learning. IEEE Access 12 (2023), 2045--2056. https://doi.org/10.1109/ACCESS.2023.3347619
[2]
Javed Ashraf, Asim D Bakhshi, Nour Moustafa, Hasnat Khurshid, Abdullah Javed, and Amin Beheshti. 2020. Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22, 7 (2020), 4507--4518.
[3]
M. Bhavsar, Y. Bekele, K. Roy, J. Kelly, and D. Limbrick. 2024. FL-IDS: Federated learning-based intrusion detection system using edge devices for transportation IoT. IEEE Access 12 (2024), 52215--52226. https://doi.org/10.1109/ACCESS.2024. 3386631
[4]
M. J. Choi, I. R. Jeong, and H. M. Song. 2024. Fast and efficient context-aware embedding generation using fuzzy hashing for in-vehicle network intrusion detection. Vehicular Communications 47 (2024), 100786. https://doi.org/10.1016/j. vehcom.2024.100786
[5]
G. Van den Broeck, A. Lykov, M. Schleich, and D. Suciu. 2022. On the tractability of SHAP explanations. Journal of Artificial Intelligence Research 74 (2022), 851--886. https://doi.org/10.1613/jair.1.13283
[6]
Araya Kibrom Desta, Shuji Ohira, Ismail Arai, and Kazutoshi Fujikawa. 2022. Rec-CNN: In-vehicle networks intrusion detection using convolutional neural networks trained on recurrence plots. Vehicular Communications 35 (2022), 100470.
[7]
W. Ding, I. Alrashdi, H. Hawash, and M. Abdel-Basset. 2024. DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks. Information Sciences 658 (2024), 120057. https://doi.org/10. 1016/j.ins.2023.120057
[8]
Koyel Datta Gupta, Deepak Kumar Sharma, Rinky Dwivedi, and Gautam Srivastava. 2023. AHDNN: attention-enabled hierarchical deep neural network framework for enhancing security of connected and autonomous vehicles. Journal of Circuits, Systems and Computers 32, 04 (2023), 2350058.
[9]
A. Haddaji, S. Ayed, and L. C. Fourati. 2024. A novel and efficient framework for in-vehicle security enforcement. Ad Hoc Networks 158 (2024), 103481. https: //doi.org/10.1016/j.adhoc.2024.103481
[10]
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the Knowledge in a Neural Network. arXiv:1503.02531 [stat.ML] https://arxiv.org/abs/1503.02531
[11]
Thien-Nu Hoang and Daehee Kim. 2024. Supervised contrastive ResNet and transfer learning for the in-vehicle intrusion detection system. Expert Systems with Applications 238 (2024), 122181.
[12]
Md Delwar Hossain, Hiroyuki Inoue, Hideya Ochiai, Doudou Fall, and Youki Kadobayashi. 2020. LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications. IEEE Access 8 (2020), 185489--185502. https://doi.org/10. 1109/ACCESS.2020.3029307
[13]
ZAE Houda, B Brik, L Khoukhi, SG Cetin, C Goztepe, GK Kurt, H Yanikomeroglu, K Zia, A Chiumento, PJM Havinga, et al. 2022. Big Data and Machine Learning for Communications 1164 'Why Should I Trust Your IDS'': An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks. (2022).
[14]
Narayan Khatri, Sihyung Lee, and Seung Yeob Nam. 2023. Transfer LearningBased Intrusion Detection System for a Controller Area Network. IEEE Access 11 (2023), 120963--120982. https://doi.org/10.1109/ACCESS.2023.3328182
[15]
Tien-Dat Le, Hoang Bao Huy Truong, Van Phu Pham, and Daehee Kim. 2024. Multi-classification in-vehicle intrusion detection system using packet- and sequence-level characteristics from time-embedded transformer with autoencoder. Knowledge-Based Systems 299 (2024), 112091. https://doi.org/10.1016/j. knosys.2024.112091
[16]
H. C. Lin, P. Wang, K. M. Chao, W. H. Lin, and J. H. Chen. 2022. Using deep learning networks to identify cyber attacks on intrusion detection for in-vehicle networks. Electronics 11, 14 (2022), 2180. https://doi.org/10.3390/electronics11142180
[17]
W. Lo, H. Alqahtani, K. Thakur, A. Almadhor, S. Chander, and G. Kumar. 2022. A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic. Vehicular Communications 35 (2022), 100471. https://doi.org/10.1016/j.vehcom.2022.100471
[18]
S. M. Lundberg and S.-I. Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017). https: //doi.org/10.48550/arXiv.1705.07874
[19]
AV Shreyas Madhav and Amit Kumar Tyagi. 2022. Explainable Artificial Intelligence (XAI): connecting artificial decision-making and human trust in autonomous vehicles. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021. Springer, 123--136.
[20]
Sk Tanzir Mehedi, Adnan Anwar, Ziaur Rahman, and Kawsar Ahmed. 2021. Deep transfer learning based intrusion detection system for electric vehicular networks. Sensors 21, 14 (2021), 4736.
[21]
Minki Nam, Seungyoung Park, and Duk Soo Kim. 2021. Intrusion Detection Method Using Bi-Directional GPT for in-Vehicle Controller Area Networks. IEEE Access 9 (2021), 124931--124944. https://doi.org/10.1109/ACCESS.2021.3110524
[22]
Euclides Carlos Pinto Neto, Hamideh Taslimasa, Sajjad Dadkhah, Shahrear Iqbal, Pulei Xiong, Taufiq Rahman, and Ali A. Ghorbani. 2024. CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus. Internet of Things 26 (2024), 101209. https://doi.org/10.1016/j.iot.2024.101209
[23]
Trieu Phong Nguyen, Heungwoo Nam, and Daehee Kim. 2023. Transformerbased attention network for in-vehicle intrusion detection. IEEE Access 11 (2023), 55389--55403.
[24]
Mert D Pesé, Jay W Schauer, Junhui Li, and Kang G Shin. 2021. S2-CAN: Sufficiently secure controller area network. In Proceedings of the 37th Annual Computer Security Applications Conference. 425--438.
[25]
Adnan Qayyum, Muhammad Usama, Junaid Qadir, and Ala Al-Fuqaha. 2020. Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward. IEEE Communications Surveys & Tutorials 22, 2 (2020), 998--1026.
[26]
Hongmao Qin, Mengru Yan, and Haojie Ji. 2021. Application of Controller Area Network (CAN) bus anomaly detection based on time series prediction. Vehicular Communications 27 (2021), 100291. https://doi.org/10.1016/j.vehcom.2020.100291
[27]
S. Rajapaksha, H. Kalutarage, M. O. Al-Kadri, A. Petrovski, G. Madzudzo, and M. Cheah. 2023. AI-based Intrusion Detection Systems for In-Vehicle Networks: A Survey. Comput. Surveys 55, 11 (2023), 1--40. https://doi.org/10.1145/3570954
[28]
Alessandro Renda, Pietro Ducange, Francesco Marcelloni, Dario Sabella, Miltiadis C. Filippou, Giovanni Nardini, Giovanni Stea, Antonio Virdis, Davide Micheli, Damiano Rapone, and Leonardo Gomes Baltar. 2022. Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking. Information 13, 8 (2022). https://doi.org/10.3390/info13080395
[29]
Hyun Min Song, Jiyoung Woo, and Huy Kang Kim. 2020. In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications 21 (2020), 100198.
[30]
Pengfei Sun, Pengju Liu, Qi Li, Chenxi Liu, Xiangling Lu, Ruochen Hao, and Jinpeng Chen. 2020. DL-IDS: Extracting Features Using CNN-LSTM Hybrid Network for Intrusion Detection System. Security and Communication Networks 2020, 1 (2020), 8890306. https://doi.org/10.1155/2020/8890306
[31]
Anum Talpur and Mohan Gurusamy. 2021. Machine Learning for Security in Vehicular Networks: A Comprehensive Survey. CoRR abs/2105.15035 (2021). arXiv:2105.15035 https://arxiv.org/abs/2105.15035
[32]
S. Ullah, M. A. Khan, J. Ahmad, S. S. Jamal, Z. e Huma, M. T. Hassan, and W. J. Buchanan. 2022. HDL-IDS: a hybrid deep learning architecture for intrusion detection in the Internet of Vehicles. Sensors 22, 4 (2022), 1340. https://doi.org/ 10.3390/s22041340
[33]
Y. Wang, G. Qin, M. Zou, Y. Liang, G. Wang, K. Wang, and Z. Zhang. 2024. A lightweight intrusion detection system for internet of vehicles based on transfer learning and MobileNetV2 with hyper-parameter optimization. Multimedia Tools and Applications 83, 8 (2024), 22347--22369. https://doi.org/10.1007/s11042-023- 14578--1
[34]
L. Yang and A. Shami. 2022. A transfer learning and optimized CNN based intrusion detection system for Internet of Vehicles. In ICC 2022 - IEEE International Conference on Communications. IEEE, New York, NY, USA, 2774--2779. https://doi.org/10.1109/ICC4585.2022.9839780
[35]
Hao Zhao, Yaokai Feng, Hiroshi Koide, and Kouichi Sakurai. 2019. An ANN Based Sequential Detection Method for Balancing Performance Indicators of IDS. In 2019 Seventh International Symposium on Computing and Networking (CANDAR). 239--244. https://doi.org/10.1109/CANDAR.2019.00039
Index Terms
- Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI
Recommendations
AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey
The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to ...
A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic
AbstractA significant increase in the use of electronics control units (ECUs) in modern vehicles has made controller area network (CAN) a de facto standard in the automotive industry. CAN standard has been designed as a reliable and ...
In-vehicle network intrusion detection using deep convolutional neural network
AbstractThe implementation of electronics in modern vehicles has resulted in an increase in attacks targeting in-vehicle networks; thus, attack detection models have caught the attention of the automotive industry and its researchers. Vehicle ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2024
149 pages
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 November 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
CCS '24
Sponsor:
CCS '24: ACM SIGSAC Conference on Computer and Communications Security
October 14 - 18, 2024
UT, Salt Lake City, USA
Upcoming Conference
CCS '25
- Sponsor:
- sigsac
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 123Total Downloads
- Downloads (Last 12 months)123
- Downloads (Last 6 weeks)26
Reflects downloads up to 08 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in