为实现网络控制系统(Networked Control Systems,NCS)中重放攻击的检测,在现有研究利用物理水印检测重放攻击的启发下,设计了利用主动丢包对重放攻击进行实时检测的方法 .首先,在理论层面上,利用系统输出的残差构建检测函数,并通过受攻...为实现网络控制系统(Networked Control Systems,NCS)中重放攻击的检测,在现有研究利用物理水印检测重放攻击的启发下,设计了利用主动丢包对重放攻击进行实时检测的方法 .首先,在理论层面上,利用系统输出的残差构建检测函数,并通过受攻击前后检测函数的变化,证明该检测方法的有效性.然后,以一辆四轮汽车为被控对象,比较车辆受攻击前后速度与检测函数的变化.最后,综合考虑车辆对重放攻击的检测结果与速度跟踪结果,确定车辆的最优主动丢包率的范围区间.结果表明:加入主动丢包前,车辆受到重放攻击时,速度会发生剧烈变化而检测函数几乎没有变化;加入主动丢包后,车辆受到重放攻击时,速度剧烈变化的同时检测函数也产生了剧烈的变化;主动丢包率为12%~16%时,系统既能够准确地检测出重放攻击,又能够保证车辆平稳行驶,为后续的重放攻击检测研究提供了参考.展开更多
针对信息物理系统下的虚假数据注入攻击(False Data Injection Attack, FDIA)中的随机攻击和隐蔽攻击,基于自适应卡尔曼滤波研究了攻击检测问题。常用的卡方检测可以有效检测出FDIA中的随机攻击,但是具有隐蔽性的FDIA可以绕过错误数据...针对信息物理系统下的虚假数据注入攻击(False Data Injection Attack, FDIA)中的随机攻击和隐蔽攻击,基于自适应卡尔曼滤波研究了攻击检测问题。常用的卡方检测可以有效检测出FDIA中的随机攻击,但是具有隐蔽性的FDIA可以绕过错误数据检测机制,使得卡方检测失败。由此在卡方检测的基础上结合相似性检测,针对系统噪声的时变特性,基于自适应卡尔曼滤波提出新的检测方法。该算法解决了实际噪声不确定性对系统的影响,且能有效检测FDIA中的随机攻击和隐蔽攻击。通过仿真验证了该方法的有效性。展开更多
The Safe Drinking Water Act (SDWA) mandates that the drinking water should be monitored for 226Ra and 228Ra isotopes and establishes the Maximum Contaminant Level of 185 mBq/L (5 pCi·L-1) for the sum. In addition...The Safe Drinking Water Act (SDWA) mandates that the drinking water should be monitored for 226Ra and 228Ra isotopes and establishes the Maximum Contaminant Level of 185 mBq/L (5 pCi·L-1) for the sum. In addition, SDWA regulates the Detection Limit (DL) of 37.0 mBq/L (1 pCi/L) for each isotope. The purpose of this work is to develop a working method for the determination of radium isotopes in drinking water satisfying the regulatory requirements of U.S. Environmental Protection Agency by utilizing our extensive experience in low-background gamma spectrometry at this laboratory. Two versions of the method were studied: destructive and non-destructive. Destructive method used the BaSO4 coprecipitation as well as 133Ba tracer for chemical recovery. We have used three gamma spectrometers: low-background 102% and 134% efficient with top muon guards, as well as an ultralow-background 140% efficient with full muon guard. We obtained a range of DLs from 5.3 to 22.6 mBq/L for 226Ra and from 7.4 to 30.4 mBq/L for 228Ra using the destructive method. For non-destructive method, the DL range was 26.0 to 26.9 mBq/L for 226Ra and 27.6 to 28.6 mBq/L for 228Ra using the 140% detector. To verify the methods, 7 to 10 laboratory control samples were spiked with both 226Ra and 228Ra at two different activities of 37.0 and 185 mBq/L. The results were evaluated by performing a combined location/variance chi-square test at a right-tail significance of 0.01 (99% Confidence Level), as stipulated by EPA. The verification results passed the chi-square tests at both activity levels. The destructive method can be accomplished using low-background gamma spectrometry, whereas non-destructive method requires ultralow-background gamma spectrometry.展开更多
BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver dise...BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver disease.This could be attributed to many factors,among which are human habits,awareness issues,poor healthcare,and late detection.To curb the growing threats from liver disease,early detection is critical to help reduce the risks and improve treatment outcome.Emerging technologies such as machine learning,as shown in this study,could be deployed to assist in enhancing its prediction and treatment.AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection,diagnosis,and reduction of risks and mortality associated with the disease.METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history.The data were collected from the state of Andhra Pradesh,India,through https://www.kaggle.com/datasets/uciml/indian-liver-patientrecords.The population was divided into two sets depending on the disease state of the patient.This binary information was recorded in the attribute"is_patient".RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36%and 73.24%,respectively,which was much better than the conventional method.The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis(scarring)and to enhance the survival of patients.The study showed the potential of machine learning in health care,especially as it concerns disease prediction and monitoring.CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease.Howe展开更多
文摘为实现网络控制系统(Networked Control Systems,NCS)中重放攻击的检测,在现有研究利用物理水印检测重放攻击的启发下,设计了利用主动丢包对重放攻击进行实时检测的方法 .首先,在理论层面上,利用系统输出的残差构建检测函数,并通过受攻击前后检测函数的变化,证明该检测方法的有效性.然后,以一辆四轮汽车为被控对象,比较车辆受攻击前后速度与检测函数的变化.最后,综合考虑车辆对重放攻击的检测结果与速度跟踪结果,确定车辆的最优主动丢包率的范围区间.结果表明:加入主动丢包前,车辆受到重放攻击时,速度会发生剧烈变化而检测函数几乎没有变化;加入主动丢包后,车辆受到重放攻击时,速度剧烈变化的同时检测函数也产生了剧烈的变化;主动丢包率为12%~16%时,系统既能够准确地检测出重放攻击,又能够保证车辆平稳行驶,为后续的重放攻击检测研究提供了参考.
文摘针对信息物理系统下的虚假数据注入攻击(False Data Injection Attack, FDIA)中的随机攻击和隐蔽攻击,基于自适应卡尔曼滤波研究了攻击检测问题。常用的卡方检测可以有效检测出FDIA中的随机攻击,但是具有隐蔽性的FDIA可以绕过错误数据检测机制,使得卡方检测失败。由此在卡方检测的基础上结合相似性检测,针对系统噪声的时变特性,基于自适应卡尔曼滤波提出新的检测方法。该算法解决了实际噪声不确定性对系统的影响,且能有效检测FDIA中的随机攻击和隐蔽攻击。通过仿真验证了该方法的有效性。
文摘The Safe Drinking Water Act (SDWA) mandates that the drinking water should be monitored for 226Ra and 228Ra isotopes and establishes the Maximum Contaminant Level of 185 mBq/L (5 pCi·L-1) for the sum. In addition, SDWA regulates the Detection Limit (DL) of 37.0 mBq/L (1 pCi/L) for each isotope. The purpose of this work is to develop a working method for the determination of radium isotopes in drinking water satisfying the regulatory requirements of U.S. Environmental Protection Agency by utilizing our extensive experience in low-background gamma spectrometry at this laboratory. Two versions of the method were studied: destructive and non-destructive. Destructive method used the BaSO4 coprecipitation as well as 133Ba tracer for chemical recovery. We have used three gamma spectrometers: low-background 102% and 134% efficient with top muon guards, as well as an ultralow-background 140% efficient with full muon guard. We obtained a range of DLs from 5.3 to 22.6 mBq/L for 226Ra and from 7.4 to 30.4 mBq/L for 228Ra using the destructive method. For non-destructive method, the DL range was 26.0 to 26.9 mBq/L for 226Ra and 27.6 to 28.6 mBq/L for 228Ra using the 140% detector. To verify the methods, 7 to 10 laboratory control samples were spiked with both 226Ra and 228Ra at two different activities of 37.0 and 185 mBq/L. The results were evaluated by performing a combined location/variance chi-square test at a right-tail significance of 0.01 (99% Confidence Level), as stipulated by EPA. The verification results passed the chi-square tests at both activity levels. The destructive method can be accomplished using low-background gamma spectrometry, whereas non-destructive method requires ultralow-background gamma spectrometry.
文摘BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver disease.This could be attributed to many factors,among which are human habits,awareness issues,poor healthcare,and late detection.To curb the growing threats from liver disease,early detection is critical to help reduce the risks and improve treatment outcome.Emerging technologies such as machine learning,as shown in this study,could be deployed to assist in enhancing its prediction and treatment.AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection,diagnosis,and reduction of risks and mortality associated with the disease.METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history.The data were collected from the state of Andhra Pradesh,India,through https://www.kaggle.com/datasets/uciml/indian-liver-patientrecords.The population was divided into two sets depending on the disease state of the patient.This binary information was recorded in the attribute"is_patient".RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36%and 73.24%,respectively,which was much better than the conventional method.The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis(scarring)and to enhance the survival of patients.The study showed the potential of machine learning in health care,especially as it concerns disease prediction and monitoring.CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease.Howe
