Engineering Advances

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Article http://dx.doi.org/10.26855/ea.2022.12.006

Research on the Characteristics and Detection Methods of DDoS Attacks on Wireless Sensor Networks for Vehicle Networking

Xiaofen Fang1, Kunli Fang1, Guohua Li2, Xinjun Jin1,*, Lihui Zheng1

1School of Mechanical and Electrical Engineering, Quzhou Vocational and Technical College, Quzhou 324000, China.

2Quzhou Haixi Electronic Technology Co., Ltd, Quzhou 324000, China.

*Corresponding author: Xinjun Jin

Published: January 12,2023

Abstract

Smart vehicles constitute the intelligent transportation system, the complex traffic network of multiple types of sensors in the energy consumption data and the amount of data transmitted is increasing, the network consisting of multi-source wireless sensors in the vehicle is often subject to DDoS attacks, the DDoS will lead to data loss or even traffic failure. Since multiple distributed vehicle nodes are dynamic constantly entering or leaving a network cluster, as smart vehicles continue to join the new wireless sensor network and obtain new identity IDs based on location, IP addresses are always allocated and recycled. DDoS attacks against vehicle networking clusters are difficult to identify, destructive and easy to implement. In this paper, we analyze the topology and communication patterns of wireless sensor networks in vehicular networks, the characteristics of being subject to DDoS attacks, the detection methods of each, and propose the initial detection and energy consumption trust value calculation for the detection of DDoS attack network nodes.

References

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How to cite this paper

Research on the Characteristics and Detection Methods of DDoS Attacks on Wireless Sensor Networks for Vehicle Networking

How to cite this paper: Xiaofen Fang, Kunli Fang, Guohua Li, Xinjun Jin, Lihui Zheng. (2022). Research on the Characteristics and Detection Methods of DDoS Attacks on Wireless Sensor Networks for Vehicle Networking. Engineering Advances2(2), 175-181.

DOI: http://dx.doi.org/10.26855/ea.2022.12.006