Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience
- URL: http://arxiv.org/abs/2401.01394v1
- Date: Tue, 2 Jan 2024 17:36:07 GMT
- Title: Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience
- Authors: Sagar Dasgupta, Abdullah Ahmed, Mizanur Rahman, Thejesh N. Bandi,
- Abstract summary: This study explores a stealthy slow drift GPS spoofing attack, replicating the victim AV's satellite reception pattern.
The attack is designed to gradually deviate from the correct route, making real-time detection challenging.
Changing the pseudo ranges confuses the AV, leading it to incorrect destinations while remaining oblivious to the manipulation.
- Score: 4.898754501085215
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Autonomous vehicles (AVs) rely on the Global Positioning System (GPS) or Global Navigation Satellite Systems (GNSS) for precise (Positioning, Navigation, and Timing) PNT solutions. However, the vulnerability of GPS signals to intentional and unintended threats due to their lack of encryption and weak signal strength poses serious risks, thereby reducing the reliability of AVs. GPS spoofing is a complex and damaging attack that deceives AVs by altering GPS receivers to calculate false position and tracking information leading to misdirection. This study explores a stealthy slow drift GPS spoofing attack, replicating the victim AV's satellite reception pattern while changing pseudo ranges to deceive the AV, particularly during turns. The attack is designed to gradually deviate from the correct route, making real-time detection challenging and jeopardizing user safety. We present a system and study methodology for constructing covert spoofing attacks on AVs, investigating the correlation between original and spoofed pseudo ranges to create effective defenses. By closely following the victim vehicle and using the same satellite signals, the attacker executes the attack precisely. Changing the pseudo ranges confuses the AV, leading it to incorrect destinations while remaining oblivious to the manipulation. The gradual deviation from the actual route further conceals the attack, hindering its swift identification. The experiments showcase a robust correlation between the original and spoofed pseudo ranges, with R square values varying between 0.99 and 1. This strong correlation facilitates effective evaluation and mitigation of spoofing signals.
Related papers
- GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles [0.8906214436849204]
GPS networks are vulnerable to cyber-attacks like spoofing and jamming.
These threats are expected to intensify with the widespread deployment of AVs.
This paper proposes GPS Intrusion Detection System to detect GPS spoofing attacks on AVs.
arXiv Detail & Related papers (2024-05-14T06:55:16Z) - AdvGPS: Adversarial GPS for Multi-Agent Perception Attack [47.59938285740803]
This study investigates whether specific GPS signals can easily mislead the multi-agent perception system.
We introduce textscAdvGPS, a method capable of generating adversarial GPS signals which are also stealthy for individual agents within the system.
Our experiments on the OPV2V dataset demonstrate that these attacks substantially undermine the performance of state-of-the-art methods.
arXiv Detail & Related papers (2024-01-30T23:13:41Z) - Experimental Validation of Sensor Fusion-based GNSS Spoofing Attack
Detection Framework for Autonomous Vehicles [5.624009710240032]
We present a sensor fusion-based spoofing attack detection framework for Autonomous Vehicles.
Experiments are conducted in Tuscaloosa, AL, mimicking urban road structures.
Results demonstrate the framework's ability to detect various sophisticated spoofing attacks, even including slow drifting attacks.
arXiv Detail & Related papers (2024-01-02T17:30:46Z) - 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) - AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors.
We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories.
Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
arXiv Detail & Related papers (2022-09-19T03:34:59Z) - Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers.
Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods.
Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
arXiv Detail & Related papers (2022-07-20T19:49:09Z) - A Sensor Fusion-based GNSS Spoofing Attack Detection Framework for
Autonomous Vehicles [4.947150829838588]
This paper presents a sensor fusion based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles.
Data from multiple low-cost in-vehicle sensors are fused and fed into a recurrent neural network model.
We have combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect and classify left and right turns.
arXiv Detail & Related papers (2021-08-19T11:59:51Z) - Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles [5.579370215490055]
We have developed a prediction-based spoofing attack detection strategy using the long short-term memory (LSTM) model.
Based on the predicted distance traveled between the current location and the immediate future location, a threshold value is established.
Our analysis revealed that the prediction-based spoofed attack detection strategy can successfully detect the attack in real-time.
arXiv Detail & Related papers (2020-10-16T18:26:59Z) - Investigating Robustness of Adversarial Samples Detection for Automatic
Speaker Verification [78.51092318750102]
This work proposes to defend ASV systems against adversarial attacks with a separate detection network.
A VGG-like binary classification detector is introduced and demonstrated to be effective on detecting adversarial samples.
arXiv Detail & Related papers (2020-06-11T04:31:56Z) - Physically Realizable Adversarial Examples for LiDAR Object Detection [72.0017682322147]
We present a method to generate universal 3D adversarial objects to fool LiDAR detectors.
In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%.
This is one step closer towards safer self-driving under unseen conditions from limited training data.
arXiv Detail & Related papers (2020-04-01T16:11:04Z)
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.