The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectiv...With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.展开更多
Objective Abnormal muscle response (AMR) to the electrical stimulation of a branch of facial nerve is a specific electrophysiological feature of primary hemifacial spasm (HFS) . Although the correlation between intrao...Objective Abnormal muscle response (AMR) to the electrical stimulation of a branch of facial nerve is a specific electrophysiological feature of primary hemifacial spasm (HFS) . Although the correlation between intraoperative AMR findings and postoperative results in patients with HFS were investigated before,展开更多
This paper analyzes the main reasons of abnormal power metering device, discusses the monitoring method of abnormal state of power metering device, studies the abnormal detection measures of power metering device, in ...This paper analyzes the main reasons of abnormal power metering device, discusses the monitoring method of abnormal state of power metering device, studies the abnormal detection measures of power metering device, in order to promote the stable development of power metering device.展开更多
The abnormality monitoring model (AMM) of cracks in concrete dams is established through integrating safety monitoring theories with abnormality diagnosis methods of cracks. In addition, emphasis is placed on the infl...The abnormality monitoring model (AMM) of cracks in concrete dams is established through integrating safety monitoring theories with abnormality diagnosis methods of cracks. In addition, emphasis is placed on the influence of crack depth on crack mouth opening displacement (CMOD). A linear hypothesis is proposed for the propagation process of cracks in concrete based on the fictitious crack model (FCM). Abnormality points are detected through testing methods of dynamical structure mutation and statistical model mutation. The solution of AMM is transformed into a global optimization problem, which is solved by the particle swarm optimization (PSO) method. Therefore, the AMM of cracks in concrete dams is established and solved completely. In the end of the paper, the proposed model is validated by a typical crack at the 105 m elevation of a concrete gravity arch dam.展开更多
To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery usin...To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金supported by the National Natural Science Foundation of China(62033008,61873143)。
文摘With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.
文摘Objective Abnormal muscle response (AMR) to the electrical stimulation of a branch of facial nerve is a specific electrophysiological feature of primary hemifacial spasm (HFS) . Although the correlation between intraoperative AMR findings and postoperative results in patients with HFS were investigated before,
文摘This paper analyzes the main reasons of abnormal power metering device, discusses the monitoring method of abnormal state of power metering device, studies the abnormal detection measures of power metering device, in order to promote the stable development of power metering device.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51079046, 50909041, 50809025, 50879024)the National Science and Technology Support Plan (Grant Nos. 2008BAB29B03, 2008BAB29B06)+5 种基金the Special Fund of State Key Laboratory of China (Grant Nos. 2009586012, 2009586912, 2010585212)the Fundamental Research Funds for the Central Universities (Grant Nos. 2009B08514, 2010B20414, 2010B01414, 2010B14114)China Hydropower Engineering Consulting Group Co. Science and Technology Support Project (Grant No. CHC-KJ-2007-02)Jiangsu Province "333 High-Level Personnel Training Project" (Grant No. 2017-B08037)Graduate Innovation Program of Universities in Jiangsu Province (Grant No. CX09B_163Z)Science Foundation for The Excellent Youth Scholars of Ministry of Education of China (Grant No. 20070294023)
文摘The abnormality monitoring model (AMM) of cracks in concrete dams is established through integrating safety monitoring theories with abnormality diagnosis methods of cracks. In addition, emphasis is placed on the influence of crack depth on crack mouth opening displacement (CMOD). A linear hypothesis is proposed for the propagation process of cracks in concrete based on the fictitious crack model (FCM). Abnormality points are detected through testing methods of dynamical structure mutation and statistical model mutation. The solution of AMM is transformed into a global optimization problem, which is solved by the particle swarm optimization (PSO) method. Therefore, the AMM of cracks in concrete dams is established and solved completely. In the end of the paper, the proposed model is validated by a typical crack at the 105 m elevation of a concrete gravity arch dam.
基金Sponsored by the National Natural Science Foundation of China(Grant No.50875056)
文摘To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effectively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.
