Implementing a Robot Intrusion Prevention System (RIPS) for ROS 2

TL;DR

This work presents RIPS, a Robotic Intrusion Prevention System for ROS 2 that combines a ROS 2 monitor (RipsPy) with a rule engine (Rips) and a dedicated DSL to detect and mitigate cyber-physical threats in robotic applications. The system leverages System Modes to dynamically adapt robot behavior in response to rule evaluations, enabling actions such as disabling sensitive actuators or altering navigation. The authors demonstrate viability through a functional prototype evaluated on a TIAGo robot in a social-robot scenario, highlighting both detection effectiveness and practical performance considerations, including the bottleneck introduced by YAML-based IPC. The work advances robotics security by providing a specialized, rule-based IPS framework tailored to ROS 2’s DDS-based middleware, with concrete threat models, a programmable mitigation language, and real-world experimental validation.

Abstract

It is imperative to develop an intrusion prevention system (IPS), specifically designed for autonomous robotic systems. This is due to the unique nature of these cyber-physical systems (CPS), which are not merely typical distributed systems. These systems employ their own systems software (i.e. robotic middleware and frameworks) and execute distinct components to facilitate interaction with various sensors and actuators, and other robotic components (e.g. cognitive subsystems). Furthermore, as cyber-physical systems, they engage in interactions with humans and their physical environment, as exemplified by social robots. These interactions can potentially lead to serious consequences, including physical damage. In response to this need, we have designed and implemented RIPS, an intrusion prevention system tailored for robotic applications based on ROS 2, the framework that has established itself as the de facto standard for developing robotic applications. This manuscript provides a comprehensive exposition of the issue, the security aspects of ROS 2 applications, and the key points of the threat model we created for our robotic environment. It also describes the architecture and the implementation of our initial research prototype and a language specifically designed for defining detection and prevention rules for diverse, real-world robotic scenarios. Moreover, the manuscript provides a comprehensive evaluation of the approach, that includes a set of experiments with a real social robot executing a well known testbed used in international robotic competitions.

Figures (5)

• Figure 1: Our robot, a TIAGo made by Pal Robotics, operating in our laboratory and among our students in a public space.
• Figure 2: The general architecture of our system.
• Figure 3: The scheme of the monitor, RipsPy.
• Figure 4: State machine for the levels defined in the example file.
• Figure 5: Results of the custom benchmark created to measure the performance of the research prototype. The numbers show that the communication through the socket and YAML serialization/deserialization form the bottleneck of the system.