Abstract
Connected Autonomous Vehicles (CAV) are expected to revolutionize the transportation sector. However, given that CAV are connected to internet, they face a principal challenge to ensure security, safety and confidentiality. It is highly valuable to provide a real-time and proactive anomaly detection approach for Vehicular Ad hoc Network (VANET) exchanged data since such an approach helps to trigger prompt countermeasures to be undertaken allowing the damage avoidance. Recent machine learning methods show great efficiency, especially due to their capacity to handle nonlinear problems. However, an accurate anomaly detection in a space–time series is a challenging problem because of the heterogeneity of space–time data and the spatio-temporal correlations. An anomalous behavior can be seen as normal in different context. Thus, using one deep learning model to classify the observations into normal and abnormal or to identify the type of the anomaly is usually not efficient for large high-dimensional multi-variate time-series datasets. In this paper, we propose a stepwise method in which the time-series data are clustered on spatio-temporal clusters using Long Short Term Memory (LSTM) auto-encoder for dimension reduction and Grey Wolf Optimizer based clustering. Then, the anomaly detection is performed on each cluster apart using a hybrid method consisting of Auto-Encoder for feature extraction and Convolution Neural Network for classification. The results shows an increase in the accuracy by \(2\%\) in average and in the precision by approximately \(1.5\%\).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Miller, C., Valasek, C.: Adventures in automotive networks and control units. Def Con 21, 260–264 (2013)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Kumar, V., Chhabra, J.K., Kumar, D.: Grey wolf algorithm-based clustering technique. J. Intell. Syst. 26(1), 153–168 (2017)
Alahmed, A., Taiwo, S., Abido, M.: Implementation and evaluation of grey wolf optimization algorithm on power system stability enhancement. In: 2019 IEEE 10th GCC Conference and Exhibition (GCC). IEEE, April 2019
Ding, N., Ma, H., Zhao, C., Ma, Y., Ge, H.: Driver’s emotional state-based data anomaly detection for vehicular ad hoc networks. In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, August 2019
Garip, M.T., Lin, J., Reiher, P., Gerla, M.: SHIELDNET: n adaptive detection mechanism against vehicular botnets in VANETs. In: 2019 IEEE Vehicular Networking Conference (VNC). IEEE, December 2019
Ghaleb, F.A., Aizaini Maarof, M., Zainal, A., Rassam, M.A., Saeed, F., Alsaedi, M.: Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages. Veh. Commun. 20, 100186 (2019). https://doi.org/10.1016/j.vehcom.2019.100186
Ma, K., Wang, H.: Influence of exclusive lanes for connected and autonomous vehicles on freeway traffic flow. IEEE Access 7, 50168–50178 (2019)
Nie, L., Wang, H., Gong, S., Ning, Z., Obaidat, M.S., Hsiao, K.F.: Anomaly detection based on spatio-temporal and sparse features of network traffic in VANETs. In: 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, December 2019
Singh, P.K., Gupta, S., Vashistha, R., Nandi, S.K., Nandi, S.: Machine Learning Based Approach to Detect Position Falsification Attack in VANETs. In: Nandi, S., Jinwala, D., Singh, V., Laxmi, V., Gaur, M.S., Faruki, P. (eds.) ISEA-ISAP 2019. CCIS, vol. 939, pp. 166–178. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-7561-3_13
Coelho, M.C., Guarnaccia, C.: Driving information in a transition to a connected and autonomous vehicle environment: Impacts on pollutants, noise and safety. Transp. Res. Procedia 45, 740–746 (2020)
Fri, M., Douaioui, K., Tetouani, S., Mabrouki, C., Semma, E.A.: A DEA-ANN framework based in improved grey wolf algorithm to evaluate the performance of container terminal. In: IOP Conference Series: Materials Science and Engineering, vol. 827, p. 012040, June 2020
Khot, A., Dave, M.: Position Falsification Misbehavior Detection in VANETs. In: Marriwala, N., Tripathi, C.C., Kumar, D., Jain, S. (eds.) Mobile Radio Communications and 5G Networks. LNNS, vol. 140, pp. 487–499. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7130-5_39
Kopelias, P., Demiridi, E., Vogiatzis, K., Skabardonis, A., Zafiropoulou, V.: Connected and autonomous vehicles - environmental impacts - a review. Sci. Total Environ. 712, 135237 (2020)
Oucheikh, R., Fri, M., Fedouaki, F., Hain, M.: Deep real-time anomaly detection for connected autonomous vehicles. Procedia Comput. Sci. 177, 456–461 (2020). https://doi.org/10.1016/j.procs.2020.10.062
Peri, N., et a l.: Towards real-time systems for vehicle re-identification, multi-camera tracking, and anomaly detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, June 2020. https://doi.org/10.1109/cvprw50498.2020.00319
Qu, X., Yu, Y., Zhou, M., Lin, C.T., Wang, X.: Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach. Appl. Energy 257, 114030 (2020)
Wang, W., et al.: Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles. IEEE Trans. Intell. Transp. Syst. 21, 1–10 (2020). https://doi.org/10.1109/TITS.2020.2995856
WY Department, of Transportation: WY DOT Connected Vehicle Pilot: Improving Safety and Travel Reliability on 1–80 in W (2020). https://wydotcvp.wyoroad.info
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Oucheikh, R., Fri, M., Fedouaki, F., Hain, M. (2021). Deep Anomaly Detector Based on Spatio-Temporal Clustering for Connected Autonomous Vehicles. In: Foschini, L., El Kamili, M. (eds) Ad Hoc Networks. ADHOCNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-67369-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-67369-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67368-0
Online ISBN: 978-3-030-67369-7
eBook Packages: Computer ScienceComputer Science (R0)