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ROS2-Based Simulation Framework for Cyberphysical Security Analysis of UAVs

Unmesh Patil, Akshith Gunasekaran, Rakesh Bobba, Houssam Abbas

TL;DR

A simulation framework based on ROS2 is proposed, leveraging some of its key advantages, including modularity, replicability, customization, and the utilization of open-source tools such as Gazebo, which has a built-in motion planner, controller, communication models and attack models.

Abstract

We present a new simulator of Uncrewed Aerial Vehicles (UAVs) that is tailored to the needs of testing cyber-physical security attacks and defenses. Recent investigations into UAV safety have unveiled various attack surfaces and some defense mechanisms. However, due to escalating regulations imposed by aviation authorities on security research on real UAVs, and the substantial costs associated with hardware test-bed configurations, there arises a necessity for a simulator capable of substituting for hardware experiments, and/or narrowing down their scope to the strictly necessary. The study of different attack mechanisms requires specific features in a simulator. We propose a simulation framework based on ROS2, leveraging some of its key advantages, including modularity, replicability, customization, and the utilization of open-source tools such as Gazebo. Our framework has a built-in motion planner, controller, communication models and attack models. We share examples of research use cases that our framework can enable, demonstrating its utility.

ROS2-Based Simulation Framework for Cyberphysical Security Analysis of UAVs

TL;DR

A simulation framework based on ROS2 is proposed, leveraging some of its key advantages, including modularity, replicability, customization, and the utilization of open-source tools such as Gazebo, which has a built-in motion planner, controller, communication models and attack models.

Abstract

We present a new simulator of Uncrewed Aerial Vehicles (UAVs) that is tailored to the needs of testing cyber-physical security attacks and defenses. Recent investigations into UAV safety have unveiled various attack surfaces and some defense mechanisms. However, due to escalating regulations imposed by aviation authorities on security research on real UAVs, and the substantial costs associated with hardware test-bed configurations, there arises a necessity for a simulator capable of substituting for hardware experiments, and/or narrowing down their scope to the strictly necessary. The study of different attack mechanisms requires specific features in a simulator. We propose a simulation framework based on ROS2, leveraging some of its key advantages, including modularity, replicability, customization, and the utilization of open-source tools such as Gazebo. Our framework has a built-in motion planner, controller, communication models and attack models. We share examples of research use cases that our framework can enable, demonstrating its utility.
Paper Structure (10 sections, 4 figures, 2 tables)

This paper contains 10 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Related Works
  3. Design Goals
  4. Simulation Framework
  5. World Toggles
  6. Modules
  7. Implementation:
  8. Extensions
  9. Use Cases
  10. Conclusion

Figures (4)

  • Figure 1: An overview of core features of the framework. Features are classified into four broad categories. All the listed features and corresponding examples are available off the shelf.
  • Figure 2: Online mode: The figure on the top shows a manually controlled UAV in Gazebo world which simulates two camera sensors (Front and bottom (blue colour)), IMU, GPS, and SONAR sensor. Figure in the bottom shows an example ROS2 Node graph for a simple IMU spoofing scenario. All the sensor topics are listed on the left. The imu_spoof node subscribes to the IMU topic and calculates the effect of attack using attack model. This node also sends velocity commands to simulate the effect in real time.
  • Figure 3: Offline mode: The figure on the top shows RViz visualization of four autonomous UAVs with global and local trajectories. The figure in the bottom is a ROS2 Node graph showing a service-client implementation of simulation. The drone1 sim service is called by a client node. The simulation node publishes global and local trajectories and subscribes to nearby obstacles. The obstacle generator node publishes obstacles, which are then detected by a detector node. This bloack repeats for each UAV.
  • Figure 4: Overview of different interfaces used in the simulation framework. The flowchart on the top shows the topic like structure of the framework, the second flowchart indicates a simple server-client structure for the simulation. The last flowchart shows the typical structure of a ROS2 action.