Predicting Congestion Attack of Variable Spoofing Frequency for Reliable Traffic Signal System

Xiang, Yingxiao; Chen, Tong; Li, Yike; Tian, Yunzhe; Niu, Wenjia; Tong, Endong; Liu, Jiqiang; Jia, Bowei; Wu, Yalun; Huang, Xiaojun

doi:10.1007/978-3-030-96791-8_16

Cited by 2 publications

(1 citation statement)

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“…The authors of [ 24 ] highlighted that the existing solutions for identifying congestion attacks have the disadvantage of delay. Considering that a congestion attack is made continuously and periodically, the authors developed a solution for predicting congestion attacks and attack frequency and the timely forecast of future congestion attacks.…”

Section: Related Workmentioning

confidence: 99%

An Evidence Theoretic Approach for Traffic Signal Intrusion Detection

Chowdhury,

Karmakar,

Kamruzzaman

et al. 2023

Sensors

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The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon’s entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.

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Section: Related Workmentioning

confidence: 99%