An Innovative Attack Modelling and Attack Detection Approach for a
Waiting Time-based Adaptive Traffic Signal Controller
- URL: http://arxiv.org/abs/2108.08627v1
- Date: Thu, 19 Aug 2021 11:44:21 GMT
- Title: An Innovative Attack Modelling and Attack Detection Approach for a
Waiting Time-based Adaptive Traffic Signal Controller
- Authors: Sagar Dasgupta, Courtland Hollis, Mizanur Rahman, Travis Atkison
- Abstract summary: This paper introduces an innovative "slow poisoning" cyberattack for a waiting time based ATSC algorithm and a corresponding detection strategy.
We have generated a "slow poisoning" attack modeling strategy using a microscopic traffic simulator.
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.
- Score: 2.561780132629278
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks.
Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
arXiv Detail & Related papers (2024-11-01T00:37:00Z) - CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation [37.89720165358964]
This paper showcases CARACAS, a vehicular model, including component control via CAN messages and attack injection capabilities.
CarACAS showcases the efficacy of this methodology, including a Battery Electric Vehicle (BEV) model, and focuses on attacks targeting torque control in two distinct scenarios.
arXiv Detail & Related papers (2024-06-11T10:16:55Z) - Detecting stealthy cyberattacks on adaptive cruise control vehicles: A
machine learning approach [5.036807309572884]
More insidious attacks, which only slightly alter driving behavior, can result in network-wide increases in congestion, fuel consumption, and even crash risk without being easily detected.
We present a traffic model framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, false data injection attacks on sensor measurements, and denial-of-service (DoS) attacks.
A novel generative adversarial network (GAN)-based anomaly detection model is proposed for real-time identification of such attacks using vehicle trajectory data.
arXiv Detail & Related papers (2023-10-26T01:22:10Z) - Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems [8.561553195784017]
This paper evaluates the security of the deep neural network based ACC systems under runtime perception attacks.
We present a context-aware strategy for the selection of the most critical times for triggering the attacks.
We evaluate the effectiveness of the proposed attack using an actual vehicle, a publicly available driving dataset, and a realistic simulation platform.
arXiv Detail & Related papers (2023-07-18T03:12:03Z) - Reinforcement Learning based Cyberattack Model for Adaptive Traffic
Signal Controller in Connected Transportation Systems [61.39400591328625]
In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time.
This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes.
One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network.
An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
arXiv Detail & Related papers (2022-10-31T20:12:17Z) - Dynamics-aware Adversarial Attack of Adaptive Neural Networks [75.50214601278455]
We investigate the dynamics-aware adversarial attack problem of adaptive neural networks.
We propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient.
Our LGM achieves impressive adversarial attack performance compared with the dynamic-unaware attack methods.
arXiv Detail & Related papers (2022-10-15T01:32:08Z) - Time-to-Green predictions for fully-actuated signal control systems with
supervised learning [56.66331540599836]
This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data.
We utilize state-of-the-art machine learning models to predict future signal phases' duration.
Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
arXiv Detail & Related papers (2022-08-24T07:50:43Z) - Mixture GAN For Modulation Classification Resiliency Against Adversarial
Attacks [55.92475932732775]
We propose a novel generative adversarial network (GAN)-based countermeasure approach.
GAN-based aims to eliminate the adversarial attack examples before feeding to the DNN-based classifier.
Simulation results show the effectiveness of our proposed defense GAN so that it could enhance the accuracy of the DNN-based AMC under adversarial attacks to 81%, approximately.
arXiv Detail & Related papers (2022-05-29T22:30:32Z) - Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural
Network [6.065344547161387]
This paper attempts to solve the problem of Spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network.
The proposed model is computationally less intensive and has a simple architecture as compared to other deep learning models that utilize LSTMs and GRUs.
arXiv Detail & Related papers (2021-02-24T05:02:19Z) - Adversarial vs behavioural-based defensive AI with joint, continual and
active learning: automated evaluation of robustness to deception, poisoning
and concept drift [62.997667081978825]
Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security.
In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
arXiv Detail & Related papers (2020-01-13T13:54:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.