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.

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The use of mobile phones while driving has been a hot topic in the field of driving safety for decades. Although there are few studies on the influence of gesture control on in-vehicle secondary tasks, this study aims to investigate the impact of gesture-based mobile phone use without touching while driving from the perspective of multiple-resource workload owing to visual, auditory, cognitive, and psychomotor resource occupation. A novel gesture control technique was adopted for secondary task interactions, to recognize the gestures of drivers. An experiment was conducted to study the influences of two interaction modes, traditional touch-based mobile phone interaction and gesture-based mobile phone interaction, on driving behavior in three different cognitive level task groups. The results indicate that gesture-based mobile phone interaction can improve driving performance with regard to lateral position-keeping ability and steering wheel control; nevertheless, it has no significant impact on longitudinal metrics such as driving speed, driving speed variation, and throttle control variation. Gesture-based mobile phone interactions have a larger effect on secondary tasks with medium cognitive load but not on actual operation tasks. It was also verified that the performance of gesture-based mobile phone interaction was better in secondary mobile phone tasks such as switching (e.g., switching songs) and adjusting (e.g., adjusting volume) than the traditional interaction mode. This study provides the theoretical and experimental support for human–computer interaction using gesture-based mobile phone interactive control in future automobiles.