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

Abstract EN

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.