Abstract
The automation and intelligence of industrial manufacturing is the core of the fourth industrial revolution, and robotic arms and proprietary networked information systems are an integral part of this vision. However, with the benefits come risks that have been overlooked, and robotic arms have become a heavily attacked area. In order to improve the security of the robotic arm system, this paper proposes an intrusion detection method based on a state classification model. The closure operation process of the robotic arm is divided into five consecutive states, while a support vector machine based on the particle swarm optimization algorithm (PSO-H-SVM) classifies the operation state of the robotic arm. In the detection process, the classifier predicts the operation state of the robotic arm in real time, and the detection method determines whether the state transfer meets the logical requirements, and then determines whether the intrusion occurs. In addition, a response mechanism is proposed on the basis of the intrusion detection system to make protection measures for the robotic arm system. Finally, a physical experiment platform was built to test the intrusion detection method. The results showed that the classification accuracy of the PSO-H-SVM algorithm reached 96.02%, and the detection accuracy of the intrusion detection method reached 90%, which verified the effectiveness and reliability of the intrusion detection method.
Highlights
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 the process of detection, the real-time joint data of the robotic arm is processed into feature vectors for the input of the classifier, and the operation state of the robotic arm is predicted in real-time using the Particle swarm optimization (PSO)-H-Support vector machines (SVM) algorithm, which in turn detects whether the state transfer of the robotic arm satisfies the normal physical process logic in the robotic arm physical process anomaly detection method
For physical process logic attacks, the detection accuracy of the intrusion detection method reaches 90%, which can effectively demonstrate that the intrusion detection method based on the PSO-H-SVM state classification model and response mechanism can effectively detect the physical process logic attack against the robotic arm and mitigate the damage caused by the attack
Summary
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 [7], Li. 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. Section 3, an intrusion detection method based on machine learning is given to classify the real-time operational state of the robotic arm by PSO-H-SVM, to determine whether it conforms to the logical state of the physical process and performs intrusion detection.
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