IEEE Consumer Electronics Magazine - January/February 2023 - 115

mentioned previously have gradually been applied
in the IoV environment. Unfortunately, this gives
attackers new opportunities to take advantage of,
for example, attackers can use encrypted channels
to hide their malicious attacks, which is a very big
threat to IoV security, and an effective intrusion
detection mechanism is urgently needed. At present,
the research on malicious encrypted traffic
detection for IoV is basically in a blank, the topics
including feature extraction, identification, and
classification for encrypted traffic based on AI
technology are well worth exploring, and interested
researchers can invest in them and contribute
to the protection of IIoV security.
CONCLUSION
This article systematically introduces the
related concepts of the IoV, and briefly describes
the IoV network composition from the
perspective of the system architecture. We
also summarize the security threats and
attacks faced by the IoV network. We focus on
the current anomaly-based IDS for IoV, and
detail theapplicationof AItechnologyinIDS.
Finally, we discuss the open issues of IoV
intrusion detection research.
ACKNOWLEDGMENTS
This research is supported in part by the
National Natural Science Foundation of China
under Grant 61771153, Grant 61831007, and
Grant 61971154, and in part by the Fundamental
Research Funds for the Central Universities
under Grant 3072020CF0601.
&REFERENCES
1. B. Ji et al., " Survey on the Internet of Vehicles: Network
architectures and applications, " IEEECommun.
Standards Mag., vol. 4, no. 1, pp. 34-41, Mar. 2020.
2. W. Wu et al., " A survey of intrusion detection for invehicle
networks, " IEEETrans. Intell. Transp. Syst.,
vol. 21, no. 3, pp. 919-933, Mar. 2020.
3. R. Peng,W. Li, T. Yang, andK. Huafeng, " An Internet of
Vehicles intrusion detection system based on a
convolutional neural network, " in Proc. IEEEInt. Conf.
ParallelDistrib. Process. with Appl., BigDataCloud
Comput., Sustain. Comput. Commun., SocialComput.
Netw., 2019, pp. 1595-1599.
4. L. Yang, A. Moubayed, I. Hamieh, and A. Shami,
" Tree-based intelligent intrusion detection system in
Internet of Vehicles, " in Proc. IEEEGlobalCommun.
Conf., 2019, pp. 1-6.
5. J. Ashraf, A. D. Bakhshi, N. Moustafa, H. Khurshid,
A. Javed, and A. Beheshti, " Novel deep learningenabled
LSTM autoencoder architecture for
discovering anomalous events from intelligent
transportation systems, " IEEETrans. Intell. Transp.
Syst., vol. 22, no. 7, pp. 4507-4518, Jul. 2021,
doi: 10.1109/TITS.2020.3017882.
6. J. Liu, X. Li, L. Ye, H. Zhang, X. Du, and M. Guizani,
" BPDS: A blockchain based privacy-preserving data
sharing for electronic medical records, " in Proc. IEEE
GlobalCommun. Conf., 2018, pp. 1-6.
7. E. Seo, H. M. Song, and H. K. Kim, " GIDS: GANbased
intrusion detection system for in-vehicle network, " in Proc.
16thAnnu. Conf. Privacy, Secur. Trust, 2018, pp. 1-6.
8. Vitalkar and Rasika, " A review on intrusion detection
system in vehicular ad-hoc network using deep
learning method, " Int. J. Res. Appl. Sci. Eng. Technol.,
vol. 2020, pp. 1591-1595, 2020.
9. J. Liang, J. Chen, Y. Zhu, andR. Yu, " A novel intrusion
detection system for vehicular ad hoc networks (VANETs)
based ondifferences of traffic flow and position, " Appl.
SoftComput., vol. 75, pp. 712-727, Feb. 2019.
10. X. Wu, X. Xu, and M. Bilal, " Towards privacy protection
composition framework on Internet of Vehicles, " IEEE
Consum. Electron. Mag., early access, Jun. 28, 2021,
doi: 10.1109/MCE.2021.3092303.
11. Y. Zeng,M. Qiu, D. Zhu, Z. Xue, J. Xiong, and M. Liu,
" DeepVCM:Adeep learning based intrusion detection
method in VANET, " in Proc. IEEE5th IntlConf. BigData
Secur.Cloud,IEEEIntlConf.HighPerform. Smart
Comput., IEEEIntlConf. Intell. DataSecur., 2019,
pp. 288-293.
12. L. Nie, Z. Ning, X. Wang, X. Hu, J. Cheng, and Y. Li,
" Data-Driven intrusion detection for intelligent Internet
of Vehicles: A deep convolutional neural networkbased
method, " IEEETrans. Netw. Sci. Eng., vol. 7,
no. 4, pp. 2219-2230, Oct.-Dec. 2020.
13. A. Alshammari, M. A. Zohdy, D. Debnath, and
G. Corser, " Classification approach for intrusion
detection in vehicle systems, " Wireless Eng. Technol.,
vol. 9, no. 4, pp. 79-94, 2018.
14. G. D'Angelo, A. Castiglione, and F. Palmieri, " A
cluster-based multidimensional approach for
detecting attacks on connected vehicles, " IEEE
Internet Things J., vol. 8, no. 16, pp. 12518-12527,
Aug. 2021.
January/February 2023
115
http://dx.doi.org/10.1109/TITS.2020.3017882 http://dx.doi.org/10.1109/MCE.2021.3092303
IEEE Consumer Electronics Magazine - January/February 2023

Table of Contents for the Digital Edition of IEEE Consumer Electronics Magazine - January/February 2023
