Abstract
With the development of the Internet of Things (IoT) and automobile industry in recent years, the Internet of Vehicle (IoV) has become a future direction of automobile development. Due to the large amount of vehicles, the opening of wireless media, the high-speed movement of vehicles and the impact of the environment, it is inevitable to produce abnormal data in IoVs including data tampering, loss, disorder and so on. However, there are few systematic research results for outlier detection of IoVs. The usability of the existing outlier detection schemes and their performances are not yet evaluated. To this issue, we select six applicable schemes and propose the outlier detection process for IoVs. Then we evaluate the comparison performances of the proposed schemes on real vehicle data collected by a Focus car.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, Z., Gao, C., Xu, C., et al.: Social big-data-based content dissemination in internet of vehicles. IEEE Trans. Ind. Inf. 14(2), 768–777 (2018)
Lu, N., Cheng, N., Zhang, N., et al.: Connected vehicles: solutions and challenges. IEEE Internet Things J. 1(4), 289–299 (2014)
Praba, V.L., Ranichitra, A.: Isolating malicious vehicles and avoiding collision between vehicles in VANET. In: IEEE International Conference on Communication & Signal Processing, pp. 811–815 (2013)
Alheeti, K.A., Gruebler, A., Mcdonaldmaier, K.D.: An intrusion detection system against malicious attacks on the communication network of driverless cars. In: IEEE Consumer Communications and Networking Conference, pp. 916–921 (2015)
Zhang, M., Chen, C., Wo, T., et al.: SafeDrive: online driving anomaly detection from large-scale vehicle data. IEEE Trans. Ind. Inf. 13(4), 2087–2096 (2017)
Ebrahim, B., Ozgul, S., Muammer, E.: Exponential smoothing of multiple reference frame components with GPUs for real-time detection of time-varying harmonics and interharmonics of EAF currents. IEEE Trans. Ind. Appl. 54, 6566–6575 (2018)
Ballal, T., Suliman, M.A., Al-Naffouri, T.Y.: Bounded perturbation regularization for linear least squares estimation. IEEE Access 5, 27551–27562 (2017)
Hayashi, H., Shibanoki, T., Shima, K., et al.: A recurrent probabilistic neural network with dimensionality reduction based on time-series discriminant component analysis. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3021–3033 (2015)
Zhang, S., Li, X., et al.: Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 9, 1774–1785 (2018)
Kosasih, K., Abeyratne, U.R., Swarnkar, V., et al.: Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis. IEEE Trans. Biomed. Eng. 62(4), 1185–1194 (2015)
Xing, Y.Y., Wu, X.Y., Jiang, P., et al.: Dynamic Bayesian evaluation method for system reliability growth based on in-time correction. IEEE Trans. Reliab. 59(2), 309–312 (2010)
Acknowledgments
Part of this work has been supported by National Natural Science Foundation of China (No. 61771373, 61771374, 61601357), China 111 Project (No. B16037), in part by the Fundamental Research Fund for the Central Universities (No. JB181508, JB171501, JB181506, JB181507), and “13th Five-Year” Plan Equipment Pre-Research Foundation of China (No. 6140134040216HT76001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zeng, Y., Zhao, H., Zhang, H., Zhang, Q. (2019). Outlier Detection of Internet of Vehicles. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11637. Springer, Cham. https://doi.org/10.1007/978-3-030-24900-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-24900-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24899-4
Online ISBN: 978-3-030-24900-7
eBook Packages: Computer ScienceComputer Science (R0)