Simulation of Sensor Spoofing Attacks on Unmanned Aerial Vehicles Using the Gazebo Simulator

• URL: http://arxiv.org/abs/2309.09648v1
• Date: Mon, 18 Sep 2023 10:34:32 GMT
• Title: Simulation of Sensor Spoofing Attacks on Unmanned Aerial Vehicles Using the Gazebo Simulator
• Authors: Irdin Pekaric, David Arnold and Michael Felderer
• Abstract summary: This paper investigates possible attacks that can be simulated, and then performing their simulations. Attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. Messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle.
• Score: 4.383011485317949
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). However, this was not the case with security testing. Performing security testing in a simulator is very difficult because security attacks are performed on a different abstraction level. In addition, the attacks themselves are becoming more sophisticated, which directly contributes to the difficulty of executing them in a simulator. In this paper, we attempt to tackle the aforementioned gap by investigating possible attacks that can be simulated, and then performing their simulations. The presented approach shows that attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. This is achieved by exploiting vulnerabilities of the ROS and MAVLink protocol and injecting malicious processes into an application. As a result, messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle. This was tested in multiple scenarios, thereby proving that it is indeed possible to simulate certain attack types, such as spoofing and jamming.

Related papers

• ROS2-Based Simulation Framework for Cyberphysical Security Analysis of UAVs [0.08333024746293494]
  We present a new simulator of Uncrewed Aerial Vehicles (UAVs) that is tailored to the needs of testing cyber-physical security attacks and defenses. Our framework has a built-in motion planner, controller, communication models and attack models.
  arXiv  Detail & Related papers  (2024-10-04T23:23:54Z)
• Promptable Closed-loop Traffic Simulation [57.36568236100507]
  ProSim is a multimodal promptable closed-loop traffic simulation framework. ProSim rolls out a traffic scenario in a closed-loop manner, modeling each agent's interaction with other traffic participants. To support research on promptable traffic simulation, we create ProSim-Instruct-520k, a multimodal prompt-scenario paired driving dataset.
  arXiv  Detail & Related papers  (2024-09-09T17:59:15Z)
• UniSim: A Neural Closed-Loop Sensor Simulator [76.79818601389992]
  We present UniSim, a neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle. UniSim builds neural feature grids to reconstruct both the static background and dynamic actors in the scene. We incorporate learnable priors for dynamic objects, and leverage a convolutional network to complete unseen regions.
  arXiv  Detail & Related papers  (2023-08-03T17:56:06Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [76.46575807165729]
  We propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. By simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
  arXiv  Detail & Related papers  (2021-01-16T23:23:12Z)
• Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators [13.386879259549305]
  We show that SBST can be used to effectively and efficiently generate critical test scenarios in two simulators. We find that executing the same test scenarios in the two simulators leads to notable differences in the details of the test outputs.
  arXiv  Detail & Related papers  (2020-12-12T14:00:33Z)
• Simulating Unknown Target Models for Query-Efficient Black-box Attacks [21.21690695534498]
  adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute model to counterfeit the functionality of the target model. This study aims to train a generalized substitute model called "Simulator", which can mimic the functionality of any unknown target model.
  arXiv  Detail & Related papers  (2020-09-02T11:30:40Z)
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [88.0416857308144]
  We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
  arXiv  Detail & Related papers  (2020-08-13T17:20:02Z)
• Towards Automated Safety Coverage and Testing for Autonomous Vehicles with Reinforcement Learning [0.3683202928838613]
  Validation puts the autonomous vehicle system to the test in scenarios or situations that the system would likely encounter in everyday driving. We propose using reinforcement learning (RL) to generate failure examples and unexpected traffic situations for the AV software implementation.
  arXiv  Detail & Related papers  (2020-05-22T19:00:38Z)

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.