To detect more attacks aiming at key security data in program behavior-based anomaly detection,the data flow properties were formulated as unary and binary relations on system call arguments.A new method named two-phr...To detect more attacks aiming at key security data in program behavior-based anomaly detection,the data flow properties were formulated as unary and binary relations on system call arguments.A new method named two-phrase analysis(2PA)is designed to analyze the efficient relation dependency,and its description as well as advantages are discussed.During the phase of static analysis,a dependency graph was constructed according to the program's data dependency graph,which was used in the phase of dynamic learning to learn specified binary relations.The constructed dependency graph only stores the information of related arguments and events,thus improves the efficiency of the learning algorithm and reduces the size of learned relation dependencies.Performance evaluations show that the new method is more efficient than existing methods.展开更多
The reactor pressure vessel(RPV)is susceptible to brittle fracture due to the influence of ion irradiation and high temperature,which presents a significant risk to the safe operation of nuclear reactors.It has been d...The reactor pressure vessel(RPV)is susceptible to brittle fracture due to the influence of ion irradiation and high temperature,which presents a significant risk to the safe operation of nuclear reactors.It has been demonstrated that pulsed electric current can effectively address the issue of embrittlement in RPV steel.However,the relationship between pulse parameters(duty ratio,frequency,current,and time)and the effectiveness of pulse current processing has not been systematically studied.The application of machine learning methods enables autonomous exploration and learning of the relationship between data.Consequently,this study proposes a machine learning method based on the random forest model to establish the relationship between the parameters of electrical pulses and the repair effect of RPV steel.A generative adversarial network is employed to enhance data diversity and scalability,while a particle swarm optimization algorithm is utilized to optimize the initialization weights and biases of the random forest model,aiming to improve the model’s fitting ability and training performance.The results indicate that the coefficient of determination R-square(R^(2)),root mean squared error and mean absolute error values are 0.934,0.045,and 0.036,respectively,suggesting that the model has the potential to predict the performance recovery of RPV steel after pulsed electric field treatment.The prediction of the impact of pulse current parameters on the repair effect will help to enhance and optimize the repair process,thereby providing a scientific basis for pulse current repair processing.展开更多
文摘To detect more attacks aiming at key security data in program behavior-based anomaly detection,the data flow properties were formulated as unary and binary relations on system call arguments.A new method named two-phrase analysis(2PA)is designed to analyze the efficient relation dependency,and its description as well as advantages are discussed.During the phase of static analysis,a dependency graph was constructed according to the program's data dependency graph,which was used in the phase of dynamic learning to learn specified binary relations.The constructed dependency graph only stores the information of related arguments and events,thus improves the efficiency of the learning algorithm and reduces the size of learned relation dependencies.Performance evaluations show that the new method is more efficient than existing methods.
基金financially supported by the National Natural Science Foundation of China(U21B2082,52474410)the National Key R&D Program of China(2023YFB3709903,2020 YFA0714900)+5 种基金the Key R&D Program of Shandong Province,China(2023CXGC010406)the Scientific Research Special Project for First-Class Disciplines in Inner Mongolia Autonomous Region(YLXKZXNKD-001)the Natural Science Foundation of Inner Mongolia Autonomous Region of China(2024ZD06)the Technology Support Project for the Construction of Major Innovation Platforms in Inner Mongolia Autonomous Region(XM2024XTGXQ16)the Beijing Municipal Natural Science Foundation(2222065)the Fundamental Research Funds for the Central Universities(FRF-TP-22-02C2).
文摘The reactor pressure vessel(RPV)is susceptible to brittle fracture due to the influence of ion irradiation and high temperature,which presents a significant risk to the safe operation of nuclear reactors.It has been demonstrated that pulsed electric current can effectively address the issue of embrittlement in RPV steel.However,the relationship between pulse parameters(duty ratio,frequency,current,and time)and the effectiveness of pulse current processing has not been systematically studied.The application of machine learning methods enables autonomous exploration and learning of the relationship between data.Consequently,this study proposes a machine learning method based on the random forest model to establish the relationship between the parameters of electrical pulses and the repair effect of RPV steel.A generative adversarial network is employed to enhance data diversity and scalability,while a particle swarm optimization algorithm is utilized to optimize the initialization weights and biases of the random forest model,aiming to improve the model’s fitting ability and training performance.The results indicate that the coefficient of determination R-square(R^(2)),root mean squared error and mean absolute error values are 0.934,0.045,and 0.036,respectively,suggesting that the model has the potential to predict the performance recovery of RPV steel after pulsed electric field treatment.The prediction of the impact of pulse current parameters on the repair effect will help to enhance and optimize the repair process,thereby providing a scientific basis for pulse current repair processing.
