4.2 Article

Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods

Journal

SECURITY AND COMMUNICATION NETWORKS
Volume 2021, Issue -, Pages -

Publisher

WILEY-HINDAWI
DOI: 10.1155/2021/1632825

Keywords

-

Funding

  1. National Natural Science Foundation of China [61972025, 61802389, 61672092, U1811264, 61966009]
  2. National Key R&D Program of China [2020YFB1005604, 2020YFB2103802]
  3. Guangxi Key Laboratory of Trusted Software [KX201902]

Ask authors/readers for more resources

This paper proposes a congestion attack detection approach by combining empirical prediction and analytical verification, which successfully predicts and verifies congestion attacks by collecting traffic images and defining traffic flow features, achieving timely and accurate congestion attack detection.
The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.2
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available