An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

Abstract

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.

An Evidence Theoretic Approach for Traffic Signal Intrusion Detection

“…Li et al [ 12 ] introduced a traffic image feature-based attack prediction mechanism. They claimed that, based on feature-based learning, it is possible to understand the relationship between attack and the caused congestion.…”

“…However, in this study, a theoretical model for intrusion detection and its evaluation and validation were not specifically performed. In the current literature, there exist congestion-based attack analysis [ 11 ] and intrusion detection techniques [ 12 ] that exploit traffic image features. Additionally, with intrusion detection considering a specific attack, the other loopholes of these approaches rely on a selected set of traffic image features and an image prepossessing technique (background separation) that usually leads to being more computationally expensive.…”

Toward Blockchain-Based Spoofing Defense for Controlled Optimization of Phases in Traffic Signal System

“…Therefore, to solve this problem, the feasible solutions may be to detect or predict such attacks after they occurred, or to prevent such attacks. In terms of detection and prediction, in some works, such as [10], [11], the spoofed traffic flow data were analyzed to detect or predict the data spoofing attack. Li et al [10] proposed a CycleGAN-based prediction approach using traffic image features that reflects the relationship between attack and the congestion caused.…”

“…In terms of detection and prediction, in some works, such as [10], [11], the spoofed traffic flow data were analyzed to detect or predict the data spoofing attack. Li et al [10] proposed a CycleGAN-based prediction approach using traffic image features that reflects the relationship between attack and the congestion caused. The authors in [11] proposed an explainable congestion attack prediction approach using a deep learning model, i.e., Tree-regularized Gated Recurrent Unit (TGRU), and they tried to explain the relationship between the traffic flow feature and the lanes in which the congestion attack vehicle locates.…”

“…Previous works mainly reveal the existence of data spoofing attacks that can cause severe traffic congestion [5], [6] Copyright c 2022 The Institute of Electronics, Information and Communication Engineers or collisions [7]. Meanwhile, in several studies [10], [11], efficient detection and prediction methods of traffic congestion caused by spoofing attacks and related client-site detection approaches [12]- [14] have been proposed. Additional studies [15]- [18] on researching the security of ITSs mainly focus on the authentication and privacy-preserving mechanisms for the connected vehicular cloud services.…”