# Research on a PSO-H-SVM-Based Intrusion Detection Method for Industrial Robotic Arms
## 1 Introduction  
With the rapid development of various fields such as network technology, communication technology, hardware and manufacturing, mankind has ushered in the fourth industrial revolution, and we are already enjoying its fruits. In particular, automation is the key development direction of industrial manufacturing, involving a number of areas such as robotics, the Internet of Things and artificial intelligence [1]. Among them, the robot is an indispensable piece of equipment in the entire link; through their study, it can be found that the manufacturing industry has become more and more intelligent in recent years, and the industrial robotic arm has played an irreplaceable role in this process. Industrial robotic arms play a major role in various fields such as manufacturing, assembly and aerospace, and have become an integral part of the industrial field. In the process of operation, they can improve efficiency, reduce costs and minimize personnel safety issues [2; 3; 4]. Traditional robotic arm systems are only used in closed operating environments and mainly rely on operators to operate, and only act as independent workstations. However, with the development of communication networks and related hardware, robotic arm systems have started to become networked [5], forming smart factories. The entire production system that relies on robotic arms is becoming more and more open, so cyber-physicalsecurity for robotic arm systems is of vital importance. The security research of robotic arm systems can effectively respond to possible threats and make the next step when a security breach is created [6]. As attacks against robotic arm systems are intensively studied, the current attacks may involve protocol attacks, sensor data attacks and control system attacks. For example, in [7], [PERSON] et al. performed a data logic attack on the EtherCAT communication protocol of a heavy-duty industrial robotic arm, mainly targeting the data packets between the control system and the actuator or between the sensor and the control system, by reordering, dropping or delaying the protocol packets to break the data logic of the communication protocol. This attack can seriously damage the operation state of the robotic arm. In [8], [PERSON] et al. proposed a sensor attack on mobile robots, using Global Positioning System (GPS) spoofing to interfere with GPS sensor readings and gyroscope sensing data spoofing through acoustic noise, ultimately leading to the crash of the unmanned aerial vehicle. In [9], [PERSON] et al. analyzed the vulnerabilities of the robot operating system (ROS), and they included an insufficient ROS authentication scheme, a ROS bag replay attack, service hijacking, etc., for which if the ROS is attacked using malware, then the control data can be tampered with, causing damage to the control system. Meanwhile, the damage caused by targeted attacks covers both physical and network domains [10], and attacks on the network or protocols will cause damage to the network domain; attacks on the control system, such as tampering with instructions or sensing data, control data spoofing attacks, etc., will not affect the network domain, but will most likely cause physical damage or even personnel damage with unimaginable consequences. Nowadays, there is a wide range of research on intrusion detection for information-physical systems, covering power systems, industrial production systems and remote-control systems [11; 12; 13; 14] these intrusion detection methods serve as important references in building the security system of robotic arms. The security of robotic arm systems is also being studied through different aspects, with some researchers having established a dynamic model of the robotic arm for fault diagnosis and isolation [15; 16], as well as adding some defensive measures against threats from external attacks through the design process of the robotic arm [17; 18]. In [19; 20], rule-based and estimation-based methods were used to detect the anomalies of the robotic arm.