Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research si...Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.展开更多
This paper describes an automated path generation method for industrial robots. Based on force control, a robotic subsystem has been developed for path automatic generation or path learning. Using a dummy tool and rou...This paper describes an automated path generation method for industrial robots. Based on force control, a robotic subsystem has been developed for path automatic generation or path learning. Using a dummy tool and roughly taught guiding points around a part contour, the robot moves in position and force controlled hybrid mode, following the order of the guiding points and with contact force direction and value predefined. During the motion, robot actual position is recorded by the robot controller. After the motion, the recorded position data is used to generate a robot path program automatically. Robot lead-through may be used in the guiding point teaching. Furthermore, a GUI （graphical user interface） is developed on the teach pedant to guide through the guiding point creation and teaching, path learning, program verification and execution. The development has been incorporated into a robotic machining product option. Combination of the robot path learning function and GUI enhances the interaction between the robot and operator and drastically increases the level of robotic ease-of-use.展开更多
文摘Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
文摘This paper describes an automated path generation method for industrial robots. Based on force control, a robotic subsystem has been developed for path automatic generation or path learning. Using a dummy tool and roughly taught guiding points around a part contour, the robot moves in position and force controlled hybrid mode, following the order of the guiding points and with contact force direction and value predefined. During the motion, robot actual position is recorded by the robot controller. After the motion, the recorded position data is used to generate a robot path program automatically. Robot lead-through may be used in the guiding point teaching. Furthermore, a GUI （graphical user interface） is developed on the teach pedant to guide through the guiding point creation and teaching, path learning, program verification and execution. The development has been incorporated into a robotic machining product option. Combination of the robot path learning function and GUI enhances the interaction between the robot and operator and drastically increases the level of robotic ease-of-use.
