A Study on Networked Industrial Robots in Smart Manufacturing: Vulnerabilities, Data Integrity Attacks and Countermeasures

The recent integration and collaboration of robots with networks expose new challenges and problems in terms of security. However, to the best of our knowledge, there has not been any comprehensive review of the vulnerabilities and attack surfaces of networked industrial robotic (NIR) systems under the framework of industrial control systems (ICS). Therefore, this paper provides an overview and further analysis of this field. We discuss the structure of the NIR systems in a modern factory, considering physical dynamics, control systems, and manufacturing networks, and then explore the vulnerabilities and the attack surface of this paradigm. Moreover, a new effort is that typical data integrity (DI) attacks that may cause serious robot equipment failures are elaborated in detail, which mainly achieves intrusion by tampering with the data of sensors or controllers through known or potential attack paths. In particular, the covert nature of several DI attacks is revealed. We analyze the existing anomaly detection strategies of the robot system and present numerical examples using dynamics-based control law to illustrate how adversaries achieve stealth to traditional detectors and the impact of the undermined robot system. Conclusively, a security framework integrating protective, detective, and responsive actions is proposed as a compensatory countermeasure for traditional methods against DI attacks. We believe that understanding these attacks and associated defense mechanisms will help accelerate the implementation of robot-based smart manufacturing technology.