ROSID: Detection of Unauthorized Nodes Using Inherent Clock Offsets in Robot Operating System

Robot Operating System (ROS) is a middleware widely used in robotics development. ROS simplifies communication between nodes within a network so that robot developers are able to simply develop robot applications. However, the communication in ROS is not secure because no authentication and encryption are provided. An unauthorized node can send malicious messages causing malfunction of robot applications. ROS2, the next version of ROS, is built on top of an existing middleware called Data Distribution Service (DDS), which incorporates security features. Unlike ROS, the communication in ROS2 can be protected if the security features are enabled. However, even with secure channels, unauthorized nodes can still pose a threat. For example, nodes with revoked certificates may still send messages because the Certificate Revocation List (CRL) is not enabled by default in ROS2, meaning that certificates might not be properly checked. Additionally, cryptographic operations in ROS2, such as encryption and authentication, can cause high latency, which could affect the performance of time-sensitive applications. For this reason, some developers are likely to disable the security features to avoid this performance issue. In this paper, we present the attack model where an unauthorized node is created and it publishes malicious messages. Also, we design a novel method (named ROSID) for detecting unauthorized nodes based on their clock offsets. Since clock offsets can serve as unique identifiers for each device, any unauthorized node sending messages would have a distinct clock offset. ROSID can be integrated into ROS applications by adding an additional device (ROSIDNode) which passively receives all messages. Accordingly, ROSID does not require any modification of existing nodes. We evaluate ROSID with several devices to verify that clock offsets can be their unique identifiers. Our evaluation results show that ROSID achieves an F1-score of 0.9703 for the node identification based on clock offsets as a fingerprint. Also, we show that ROSID is able to detect the on-target attack and the on-others attack with low false positive rates and false negative rates.