Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles

• URL: http://arxiv.org/abs/2010.11722v1
• Date: Fri, 16 Oct 2020 18:26:59 GMT
• Title: Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles
• Authors: Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury
• Abstract summary: 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.
• Score: 5.579370215490055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Global Navigation Satellite System (GNSS) provides Positioning, Navigation, and Timing (PNT) services for autonomous vehicles (AVs) using satellites and radio communications. Due to the lack of encryption, open-access of the coarse acquisition (C/A) codes, and low strength of the signal, GNSS is vulnerable to spoofing attacks compromising the navigational capability of the AV. A spoofed attack is difficult to detect as a spoofer (attacker who performs spoofing attack) can mimic the GNSS signal and transmit inaccurate location coordinates to an AV. In this study, we have developed a prediction-based spoofing attack detection strategy using the long short-term memory (LSTM) model, a recurrent neural network model. The LSTM model is used to predict the distance traveled between two consecutive locations of an autonomous vehicle. In order to develop the LSTM prediction model, we have used a publicly available real-world comma2k19 driving dataset. The training dataset contains different features (i.e., acceleration, steering wheel angle, speed, and distance traveled between two consecutive locations) extracted from the controlled area network (CAN), GNSS, and inertial measurement unit (IMU) sensors of AVs. Based on the predicted distance traveled between the current location and the immediate future location of an autonomous vehicle, a threshold value is established using the positioning error of the GNSS device and prediction error (i.e., maximum absolute error) related to distance traveled between the current location and the immediate future location. Our analysis revealed that the prediction-based spoofed attack detection strategy can successfully detect the attack in real-time.

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)
• Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience [4.898754501085215]
  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.
  arXiv  Detail & Related papers  (2024-01-02T17:36:07Z)
• 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)
• NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving [57.03126447713602]
  We present a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors. The network runs faster than real time on an embedded GPU and shows good generalization across geographic regions.
  arXiv  Detail & Related papers  (2022-09-29T01:30:34Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• 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)
• Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles [4.947150829838588]
  A sensor fusion-based attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle. 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 turns using data from the steering angle sensor. Our analysis reveals that the sensor fusion-based detection framework successfully detects all three types of spoofing attacks within the required computational latency threshold.
  arXiv  Detail & Related papers  (2021-06-05T23:02:55Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Probing Model Signal-Awareness via Prediction-Preserving Input Minimization [67.62847721118142]
  We evaluate models' ability to capture the correct vulnerability signals to produce their predictions. We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR) The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric.
  arXiv  Detail & Related papers  (2020-11-25T20:05:23Z)
• 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)

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.