Abstract
The vehicular networks are vulnerable to cyber security attacks due to the vehicles’ large attack surface. Anomaly detection is an effective means to deal with this kind of attack. Due to the vehicle’s limited computation resources, the vehicular edge network (VEN) has been proposed provide additional computing power while meeting the demand of low latency. However, the time-space limitation of edge computing prevents the vehicle data from being fully utilized. To solve this problem, a digital twin vehicular edge networks (DITVEN) is proposed. The distributed trust evaluation is established based on the trust chain transitivity and aggregation for edge computing units and digital twins to ensure the credibility of digital twins. The local reachability density and outlier factor are introduced for the time awareness anomaly detection. The curl and divergence based elements are utilized to achieve the space awareness anomaly detection. The mutual trust evaluation and anomaly detection is implemented for performance analysis, which indicates that the proposed scheme is suitable for digital twin vehicular applications.
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References
Zhang, K., Zhu, Y., Maharjan, S., Zhang, Y.: Edge intelligence and blockchain empowered 5G beyond for the industrial internet of things. IEEE Network 33(5), 12–19 (2019)
Kang, J., Lin, D., Bertino, E., Tonguz, O.: From autonomous vehicles to vehicular clouds: challenges of management, security and dependability. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 1730–1741. IEEE (2019)
Ríos, J., Mas, F., Oliva, M., Hernandez-Matias, J.: Framework to support the aircraft digital counterpart concept with an industrial design view. Int. J. Agile Syst. Manag. 9, 212–231 (2016)
Rassõlkin, A., Vaimann, T., Kallaste, A., Kuts, V.: Digital twin for propulsion drive of autonomous electric vehicle. In: 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), pp. 1–4 (2019)
Marti, S., Giuli, T.J., Lai, K., Baker, M.: Mitigating routing misbehavior in mobile ad hoc networks, pp. 255–265 (2000)
Wang, Y., Ming Chia, D.W., Ha, Y.: Vulnerability of deep learning model based anomaly detection in vehicle network. In: 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 293–296. IEEE (2020)
Bose, B., Dutta, J., Ghosh, S., Pramanick, P., Roy, S.: D&RSense: detection of driving patterns and road anomalies. In: 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), pp. 1–7. IEEE (2018)
Sassi, M.S.H., Fourati, L.C.: Investigation on deep learning methods for privacy and security challenges of cognitive IoV. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 714–720. IEEE (2020)
Reddy, V.B., Negi, A., Venkataraman, S., Venkataraman, V.R.: A similarity based trust model to mitigate badmouthing attacks in internet of things (IoT). In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 278–282 (2019)
Choi, H., Lee, G.M., Rhee, W.: Hierarchical trust chain framework for IoT services. In: 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), pp. 710–712 (2019)
Sun, W., Lei, S., Wang, L., Liu, Z., Zhang, Y.: Adaptive federated learning and digital twin for industrial internet of things. IEEE Trans. Industr. Inform. 17(8), 5605–5614 (2020)
Acknowledgment
This work is funded by the National Key R&D Program of China (2020AAA0107800), National Natural Science Foundation of China (62072184). This work is partially supported by the Project of Science and Technology Commitment of Shanghai (19511103602, 20511106002).
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Liu, J., Zhang, S., Liu, H., Zhang, Y. (2021). Distributed Collaborative Anomaly Detection for Trusted Digital Twin Vehicular Edge Networks. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12938. Springer, Cham. https://doi.org/10.1007/978-3-030-86130-8_30
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DOI: https://doi.org/10.1007/978-3-030-86130-8_30
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