Fuzzy Anomalous Rules-based Car Hacking Detection using Lasso Regression Approach
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : S. Senthil Kumar, S. Mythili
|DOI : 10.14445/22315381/IJETT-V70I5P238|
MLA Style: Senthil Kumar, S., and Mythili, S. "Fuzzy Anomalous Rules-based Car Hacking Detection using Lasso Regression Approach." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 346-356. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P238
APA Style:Senthil Kumar, S., & Mythili, S. (2022). Fuzzy Anomalous Rules-based Car Hacking Detection using Lasso Regression Approach. International Journal of Engineering Trends and Technology, 70(5), 346-356. https://doi.org/10.14445/22315381/IJETT-V70I5P238
Car hacking is the exploitation of vulnerabilities within cars` software, hardware, and communication systems. Various kinds of attacks can be injected to perform car hacking, affecting the electronic control unit to exploit the vulnerability. Predicting whether car hacking is present or not is the most difficult task. In the previous research, we introduced the Lasso Regression-based Improved Anomalous Detection Algorithm (LR-IADS).The main aim of the research work is to implementa credit card dataset to predict whether the fraudulent transaction is happening in the environment. In this research work, anomalous fuzzy rules were created initially with the help of attributes chosen from the database. Based on the Gini index, information gain, and gain ratio, we choose the attributes here. The lasso regression analysis method helps to do the rule pruning on the generated anomalous rules.
At last, unexpected suspicious detection is done according to these anomalous rules by commencing the classification process. IRVMs (Improved Relevant Vector Machines) perform it based on Association Classifiers. This research work is implemented on the car hacking database for intrusion detection gathered from the controller area network. The complete analysis of the study work is performed in a Matlab simulation platform, demonstrating that the suggested LR-IADS approach may provide accurate car hacking detection results.
Anomalous Rules, Car Hacking Detection, Gini Index, Information Gain, Relevance Vector Machine.
 B. Fleming, Advances in Automotive Electronics [Automotive Electronics], IEEE Vehicular Technology Magazine. 10(4) (2015) 4-96.
 B. Leiding, and W. V. Vorobev, Enabling the V2X Economy Revolution Using a Blockchain-based Value Transaction Layer for Vehicular Ad-hoc Networks, In MCIS. (2018) 1-33.
 J. Contreras-Castillo, S. Zeadally, and J. A. Guerrero-Ibañez, Internet of Vehicles: Architecture, Protocols, and Security, IEEE Internet of Things Journal. 5(5) (2017) 3701-3709.
 B. Groza, and P. S. Murvay, Security Solutions for the Controller Area Network: Bringing Authentication to In-Vehicle Networks, IEEE Vehicular Technology Magazine. 13(1) (2018) 40-47.
 O. Avatefipour, and H. Malik, State-of-the-art Survey on In-Vehicle Network Communication CAN-Bus Security and Vulnerabilities, arXiv preprint arXiv:1802.01725. (2018).
 Y. H. Chou, T. H. Chu, S. Y. Kuo, and C. Y. Chen, An Adaptive Emergency Broadcast Strategy for Vehicular Ad Hoc Networks, IEEE Sensors Journal. 18(12) (2017) 4814-4821.
 P. Carsten, T. R. Andel, M. Yampolskiy, and J. T. McDonald, In-Vehicle Networks: Attacks, Vulnerabilities, and Proposed Solutions, In Proceedings of the 10th Annual Cyber and Information Security Research Conference. (2015) 1-8.
 L. B. Othmane, L. Dhulipala, M. Abdelkhalek, N. Multari, and M. Govindarasu, On the Performance of Detecting Injection of Fabricated Messages into the, CAN Bus, IEEE Transactions on Dependable and Secure Computing. (2020) 1-1.
 M. Banerjee, J. Lee, and K. K. R. Choo, A Blockchain Future for the Internet of Things Security: A Position Paper, Digital Communications and Networks. 4(3) (2018) 149-160.
 R. C. Staudemeyer, H. C. Pöhls, and M. Wójcik, The Road to Privacy in IoT: Beyond Encryption and Signatures, Towards Unobservable Communication, In IEEE 19th International Symposium on" A World of Wireless, Mobile and Multimedia Networks (WoWMoM). (2018) 14-20.
 E. Vasilomanolakis, M. Krügl, C. G. Cordero, M. Mühlhäuser, and M. Fischer, Skipmon: A Locality-Aware Collaborative Intrusion Detection System, In IEEE 34th International Performance Computing and Communications Conference (IPCCC). (2015) 1-8.
 L. Nishani, and M. Biba, Machine Learning for Intrusion Detection in MANET: A State-of-the-Art Survey, Journal of Intelligent Information Systems. 46(2) (2016) 391-407.
 J. H. Seo, Detection of Car Hacking Using One-Class Classifier, Journal of the Korea Convergence Society. 9(6) (2018) 33-38.
 A. Taylor, S. Leblanc, and N. Japkowicz, Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks, In IEEE International Conference on Data Science and Advanced Analytics (DSAA). (2016) 130-139.
 Y. Jaoudi, C. Yakopcic, and T. Taha, Conversion of an Unsupervised Anomaly Detection System to Spiking Neural Network for Car Hacking Identification, In 11th International Green and Sustainable Computing Workshops (IGSC). (2020) 1-4.
 M. L. Han, J. Lee, A. R. Kang, S. Kang, J. K. Park, and H. K. Kim,A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment, In International Conference on Internet of Vehicles. (2015) 89-97.
 H. Kang, B. I. Kwak, Y. H. Lee, H. Lee, H. Lee, and H. K. Kim, Car Hacking and Defense Competition on In-Vehicle Network, In Workshop on Automotive and Autonomous Vehicle Security AutoSec. (2021) 1-25.
 F. Martinelli, F. Mercaldo, V. Nardone, and A. Santone, Car Hacking Identification through Fuzzy Logic Algorithms, In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). (2017) 1-7.
 H. Li, L. Zhao, M. Juliato, S. Ahmed, M. R. Sastry, and L. L. Yang, Poster: Intrusion Detection System for in-Vehicle Networks Using Sensor Correlation and Integration, In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. (2017) 2531-2533.
 V.S. Barletta, D. Caivano, A. Nannavecchia, and M. Scalera, Intrusion Detection for In-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach, Future Internet. 12(7) (2020) 119.
 A. Taylor, N. Japkowicz, and S. Leblanc, Frequency-Based Anomaly Detection for the Automotive CAN Bus, In World Congress on Industrial Control Systems Security (WCICSS). (2015) 45-49.
 A. Taylor, Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks Doctoral Dissertation, Universitéd` Ottawa/University of Ottawa. (2017).
 M. W. Spicer, Intrusion Detection System for Electronic Communication Buses: A New Approach Doctoral Dissertation, Virginia Tech. (2018).
 J. Jiang, M. Li, X. Jing, and B. Lv, Research on the Performance of Relevance Vector Machine for Regression and Classification, In IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). (2015) 758-762.
 [Online]. Available: https://ocslab.hksecurity.net/Datasets/CAN-intrusion-datase