The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
在逐步增加II型截尾寿命数据下,本研究深入探讨了比例危险率模型的参数以及可靠性指标的贝叶斯估计与样本预测问题。首先,通过频率方法对模型参数进行了预先估计,并分析了其相关性质。随后,在平衡损失函数框架下,本文得到了可靠性指标...在逐步增加II型截尾寿命数据下,本研究深入探讨了比例危险率模型的参数以及可靠性指标的贝叶斯估计与样本预测问题。首先,通过频率方法对模型参数进行了预先估计,并分析了其相关性质。随后,在平衡损失函数框架下,本文得到了可靠性指标的贝叶斯估计,同时也得出平衡损失比一般损失更加灵活的实用性结论。本文还进行了一系列数值模拟示例,其模拟结果与理论分析相一致。以上的理论研究和示例均证实了所提出的平衡损失下贝叶斯方法的实用性和有效性。This study delves into the Bayesian estimation and sample prediction issues of the proportional hazard rate model’s parameters and reliability indicators under progressive type-II censored lifetime data. Firstly, a preliminary estimation of the model parameters was conducted through frequency methods, and their related properties were analyzed. Subsequently, within the framework of the balanced loss function, this paper obtained the Bayesian estimation of the reliability indicators and also concluded that the balanced loss is more flexible and practical than the general loss. A series of numerical simulation examples were also conducted, and the simulation results were consistent with the theoretical analysis. The theoretical research and examples presented above all confirm the practicality and effectiveness of the proposed Bayesian method under balanced loss.展开更多
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
文摘在逐步增加II型截尾寿命数据下,本研究深入探讨了比例危险率模型的参数以及可靠性指标的贝叶斯估计与样本预测问题。首先,通过频率方法对模型参数进行了预先估计,并分析了其相关性质。随后,在平衡损失函数框架下,本文得到了可靠性指标的贝叶斯估计,同时也得出平衡损失比一般损失更加灵活的实用性结论。本文还进行了一系列数值模拟示例,其模拟结果与理论分析相一致。以上的理论研究和示例均证实了所提出的平衡损失下贝叶斯方法的实用性和有效性。This study delves into the Bayesian estimation and sample prediction issues of the proportional hazard rate model’s parameters and reliability indicators under progressive type-II censored lifetime data. Firstly, a preliminary estimation of the model parameters was conducted through frequency methods, and their related properties were analyzed. Subsequently, within the framework of the balanced loss function, this paper obtained the Bayesian estimation of the reliability indicators and also concluded that the balanced loss is more flexible and practical than the general loss. A series of numerical simulation examples were also conducted, and the simulation results were consistent with the theoretical analysis. The theoretical research and examples presented above all confirm the practicality and effectiveness of the proposed Bayesian method under balanced loss.
