# An Innovative Attack Modelling and Attack Detection Approach for a Waiting Time-based Adaptive Traffic Signal Controller

arXiv: https://arxiv.org/abs/2108.08627 · HTML: https://mlsanity.com/paper/2108.08627/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment](https://mlsanity.com/paper/1811.12620/) (2018)
- [Noticeability Versus Impact in Traffic Signal Tampering](https://mlsanity.com/paper/2002.03119/) (2020)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems](https://mlsanity.com/paper/2211.01845/) (2022)

## Abstract (arXiv)

An adaptive traffic signal controller (ATSC) combined with a connected vehicle (CV) concept uses real-time vehicle trajectory data to regulate green time and has the ability to reduce intersection waiting time significantly and thereby improve travel time in a signalized corridor. However, the CV-based ATSC increases the size of the surface vulnerable to potential cyber-attack, allowing an attacker to generate disastrous traffic congestion in a roadway network. An attacker can congest a route by generating fake vehicles by maintaining traffic and car-following rules at a slow rate so that the signal timing and phase change without having any abrupt changes in number of vehicles. Because of the adaptive nature of ATSC, it is a challenge to model this kind of attack and also to develop a strategy for detection. This paper introduces an innovative "slow poisoning" cyberattack for a waiting time based ATSC algorithm and a corresponding detection strategy. Thus, the objectives of this paper are to: (i) develop a "slow poisoning" attack generation strategy for an ATSC, and (ii) develop a prediction-based "slow poisoning" attack detection strategy using a recurrent neural network -- i.e., long short-term memory model. We have generated a "slow poisoning" attack modeling strategy using a microscopic traffic simulator -- Simulation of Urban Mobility (SUMO) -- and used generated data from the simulation to develop both the attack model and detection model. Our analyses revealed that the attack strategy is effective in creating a congestion in an approach and detection strategy is able to flag the attack.