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Federated learning based IDS approach for the IoV

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Published:23 August 2022Publication History

ABSTRACT

The Internet of Vehicles (IoV) is an Internet of Things (IoT) application that offers several utilities such as traffic analysis, safe driving, road optimization, and travel comfort. Software-Defined Networking (SDN) technology has been shown to provide various benefits to support the IoV. However, the construction of IoV makes it a complex system posing several challenges among which the important ones are security and privacy of data. Intrusion Detection Systems (IDSs) have been proposed in the IoV to identify cyber attacks and protect private data. Recently work has started to implement IDSs based on Federated learning as collaborative IDSs have proved effective security of IoV. In another hand, trust management has revolutionized the IoV filed, providing decision-making support to secure the network. Stating that an SDN-driven IoV architecture in which nodes trustworthiness gets assessed can provide a promising framework for IDS, we propose in this paper a Federated learning-based IDS for the IoV under the SDN structure. We integrate trust metrics to assist in securing the IoV network. Simulation experiments are conducted to validate the proposal.

References

  1. [1] Sharma, S., & Kaushik, B. (2022). Applications and Challenges in Internet of Vehicles: A Survey. In Internet of Things and Its Applications (pp. 55-65). Springer, Singapore.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Garcia, M. H. C., Molina-Galan, A., Boban, M., Gozalvez, J., Coll-Perales, B., Şahin, T., & Kousaridas, A. (2021). A tutorial on 5G NR V2X communications. IEEE Communications Surveys & Tutorials.Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Zamrai, M. A. H., Yusof, K. M., Azizan, M. A., Azman, M. A. A., & Hussain, S. M. (2021). A Survey on Internet of Vehicle (IoV): Applications & Comparison of VANETs, IoV and SDN-IoV.Google ScholarGoogle Scholar
  4. [4] Queiroz, A., Oliveira, E., Barbosa, M., & Dias, K. (2020, December). A Survey on Blockchain and Edge Computing applied to the Internet of Vehicles. In 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (pp. 1-6). IEEE.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Houmer, M., Ouaissa, M., Ouaissa, M., & Eddamiri, S. (2022). Applying machine learning algorithms to improve intrusion detection system in IoV. Artificial Intelligence of Things in Smart Environments: Applications in Transportation and Logistics.Google ScholarGoogle Scholar
  6. [6] Bougueroua, N., Mazouzi, S., Belaoued, M., Seddari, N., Derhab, A., & Bouras, A. (2021). A survey on multi-agent based collaborative intrusion detection systems. Journal of Artificial Intelligence and Soft Computing Research, 11(2), 111-142.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Bhattacharya, & S., Gadekallu, T. R. (2021). Federated learning for intrusion detection system: Concepts, challenges and future directions. arXiv preprint arXiv:2106.09527.Google ScholarGoogle Scholar
  8. [8] Li, W., Meng, W., & Kwok, L. F. (2021). Surveying Trust-based Collaborative Intrusion Detection: State-of-the-Art, Challenges and Future Directions. IEEE Communications Surveys & Tutorials.Google ScholarGoogle Scholar
  9. [9] Kranthi, S., Kanchana, M., & Suneetha, M. (2022). A Study of IDS-based Software-defined Networking by Using Machine Learning Concept. In Advances in Data and Information Sciences (pp. 65-79). Springer, Singapore.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Mehedi, S., Anwar, A., Rahman, Z., & Ahmed, K. (2021). Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks. Sensors, 21(14), 4736.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Zhang, T., & Zhu, Q. (2020). Differentially Private Collaborative Intrusion Detection Systems For VANETs. arXiv preprint arXiv:2005.00703.Google ScholarGoogle Scholar
  12. [12] Zhang, T., & Zhu, Q. (2018). Distributed privacy-preserving collaborative intrusion detection systems for VANETs. IEEE Transactions on Signal and Information Processing over Networks, 4(1), 148-161.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Bangui, H., Ge, M., & Buhnova, B. (2021). A hybrid machine learning model for intrusion detection in VANET. Computing, 1-29.Google ScholarGoogle Scholar
  14. [14] Yang, L., Moubayed, A., & Shami, A. (2021). MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles. IEEE Internet of Things Journal.Google ScholarGoogle Scholar
  15. [15] Jin, F., Chen, M., Zhang, W., Yuan, Y., & Wang, S. (2021). Intrusion detection on internet of vehicles via combining log-ratio oversampling, outlier detection and metric learning. Information Sciences, 579, 814-831.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Gruebler, A., McDonald-Maier, K. D., & Alheeti, K. M. A. (2015, September). An intrusion detection system against black hole attacks on the communication network of self-driving cars. In 2015 sixth international conference on emerging security technologies (EST) (pp. 86-91). IEEE.Google ScholarGoogle Scholar
  17. [17] Alladi, T., Gera, B., Agrawal, A., Chamola, V., & Yu, F. R. (2021). DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs. IEEE Transactions on Vehicular Technology, 70(11), 12013-12023.Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Ayoob, A. A., Su, G., & Al, G. (2018). Hierarchical growing neural gas network (hgng)-based semicooperative feature classifier for ids in vehicular ad hoc network (vanet). Journal of Sensor and Actuator Networks, 7(3), 41.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Yue, C., Wang, L., Wang, D., Duo, R., & Yan, H. (2021). Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network. Security and Communication Networks, 2021.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Jeong, S., Jeon, B., Chung, B., & Kim, H. K. (2021). Convolutional neural network-based intrusion detection system for AVTP streams in automotive Ethernet-based networks. Vehicular Communications, 29, 100338.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Ghimire, B., & Rawat, D. B. (2022). Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things. IEEE Internet of Things Journal.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Liu, Z., Xu, B., Cheng, B., Hu, X., & Darbandi, M. (2021). Intrusion detection systems in the cloud computing: A comprehensive and deep literature review. Concurrency and Computation: Practice and Experience, e6646.Google ScholarGoogle Scholar
  23. [23] Astillo, P. V., Duguma, D. G., Park, H., Kim, J., Kim, B., & You, I. (2022). Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System. Future Generation Computer Systems, 128, 395-405.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. [24] Ayed, M. A., & Talhi, C. Federated Learning for Anomaly-Based Intrusion Detection. In 2021 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-8). IEEE.Google ScholarGoogle Scholar
  25. [25] Zhao, R., Yin, Y., Shi, Y., & Xue, Z. (2020). Intelligent intrusion detection based on federated learning aided long short-term memory. Physical Communication, 42, 101157.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Mourad, A., Tout, H., Wahab, O. A., Otrok, H., & Dbouk, T. (2020). Ad Hoc Vehicular Fog Enabling Cooperative Low-Latency Intrusion Detection. IEEE Internet of Things Journal, 8(2), 829-843.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Abdel-Basset, M., Moustafa, N., Hawash, H., Razzak, I., Sallam, K. M., & Elkomy, O. M. (2021). Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems. IEEE Transactions on Intelligent Transportation Systems.Google ScholarGoogle Scholar
  28. [28] Ghimire, B., & Rawat, D. B. (2021). Secure, Privacy Preserving and Verifiable Federating Learning using Blockchain for Internet of Vehicles. IEEE Consumer Electronics Magazine.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Liang, H., Wu, J., Mumtaz, S., Li, J., Lin, X., & Wen, M. (2019). MBID: Micro-blockchain-based geographical dynamic intrusion detection for V2X. IEEE Communications Magazine, 57(10), 77-83.Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Shu, J., Zhou, L., Zhang, W., Du, X., & Guizani, M. (2020). Collaborative intrusion detection for VANETs: a deep learning-based distributed SDN approach. IEEE Transactions on Intelligent Transportation Systems.Google ScholarGoogle Scholar
  31. [31] Dao, N. N., Phan, T. V., Sa’ad, U., Kim, J., Bauschert, T., Do, D. T., & Cho, S. (2021). Securing heterogeneous IoT with intelligent DDoS attack behavior learning. IEEE Systems Journal.Google ScholarGoogle Scholar
  32. [32] Liang, J., Chen, J., Zhu, Y., & Yu, R. (2019). A novel Intrusion Detection System for Vehicular Ad Hoc Networks (VANETs) based on differences of traffic flow and position. Applied Soft Computing, 75, 712-727.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Other conferences
    ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security
    August 2022
    1371 pages
    ISBN:9781450396707
    DOI:10.1145/3538969

    Copyright © 2022 ACM

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    Publication History

    • Published: 23 August 2022

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    Overall Acceptance Rate228of451submissions,51%

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