A Systematic Review on Social Robots in Public Spaces: Threat Landscape and Attack Surface

There is a growing interest in using social robots in public spaces for indoor and outdoor applications. The threat landscape is an important research area being investigated and debated by various stakeholders. Objectives: This study aims to identify and synthesize empirical research on the complete threat landscape of social robots in public spaces. Specifically, this paper identifies the potential threat actors, their motives for attacks, vulnerabilities, attack vectors, potential impacts of attacks, possible attack scenarios, and mitigations to these threats. Methods: This systematic literature review follows the guidelines by Kitchenham and Charters. The search was conducted in five digital databases, and 1469 studies were retrieved. This study analyzed 21 studies that satisfied the selection criteria. Results: Main findings reveal four threat categories: cybersecurity, social, physical, and public space. Conclusion: This study completely grasped the complexity of the transdisciplinary problem of social robot security and privacy while accommodating the diversity of stakeholders’ perspectives. Findings give researchers and other stakeholders a comprehensive view by highlighting current developments and new research directions in this field. This study also proposed a taxonomy for threat actors and the threat landscape of social robots in public spaces.publishedVersio

Message Encryption in Robot Operating System: Collateral Effects of Hardening Mobile Robots

[EN] In human–robot interaction situations, robot sensors collect huge amounts of data from the environment in order to characterize the situation. Some of the gathered data ought to be treated as private, such as medical data (i.e., medication guidelines), personal, and safety information (i.e., images of children, home habits, alarm codes, etc.). However, most robotic software development frameworks are not designed for securely managing this information. This paper analyzes the scenario of hardening one of the most widely used robotic middlewares, Robot Operating System (ROS). The study investigates a robot’s performance when ciphering the messages interchanged between ROS nodes under the publish/subscribe paradigm. In particular, this research focuses on the nodes that manage cameras and LIDAR sensors, which are two of the most extended sensing solutions in mobile robotics, and analyzes the collateral effects on the robot’s achievement under different computing capabilities and encryption algorithms (3DES, AES, and Blowfish) to robot performance. The findings present empirical evidence that simple encryption algorithms are lightweight enough to provide cyber-security even in lowpowered robots when carefully designed and implemented. Nevertheless, these techniques come with a number of serious drawbacks regarding robot autonomy and performance if they are applied randomly. To avoid these issues, we define a taxonomy that links the type of ROS message, computational units, and the encryption methods. As a result, we present a model to select the optimal options for hardening a mobile robot using ROS.SIInstituto Nacional de Ciberseguridad (Adenda21)Junta de Castilla y León (LE028P17