针对物联网中传感网基础设施与应用程序分离将加重安全风险、认证机制不完善等问题,文中基于现存的互联网标准和数据包传输层安全协议DTLS(Datagram Transport Layer Security),提出了一个物联网环境下的端到端双向认证安全机制。该安...针对物联网中传感网基础设施与应用程序分离将加重安全风险、认证机制不完善等问题,文中基于现存的互联网标准和数据包传输层安全协议DTLS(Datagram Transport Layer Security),提出了一个物联网环境下的端到端双向认证安全机制。该安全机制主要利用DTLS协议的全认证握手过程实现对用户和节点身份的双向认证,并对数据进行加密和完整性封装。该机制工作在标准低功耗通信栈上面,在部署了相关协议的传感节点上对安全机制的性能参数进行了评估,如传输延时、能量消耗、内存消耗等。验证其可行性,证明了该安全机制具有低开销和高互操作性。展开更多
The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardio...The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.展开更多
机车安全信息综合监测装置为机车众多车载监测设备提供了统一的机车安全信息。随着铁路智能化的深入发展,人们对机车安全信息综合监测装置数据的安全性要求日益提高。缺乏对机车安全信息综合监测装置有效的管控手段,会导致外围设备非法...机车安全信息综合监测装置为机车众多车载监测设备提供了统一的机车安全信息。随着铁路智能化的深入发展,人们对机车安全信息综合监测装置数据的安全性要求日益提高。缺乏对机车安全信息综合监测装置有效的管控手段,会导致外围设备非法接入,造成通信总线被干扰。针对机车安全信息综合监测装置存在的问题,基于TAX18平台开展接入鉴权技术研究,提出了一套技术解决方案。在硬件上构建新的RS485通信总线架构,在软件上采用数据包传输层安全性协议(Datagram Transport Layer Security,DTLS)、mbedTLS等技术,实现机车安全信息综合监测装置相关外设的鉴权加密。应用的嵌入式领域加解密库具有较高的稳定性和可靠性,可从根本上解决外设接入问题,保障机车安全信息综合监测装置通信的可靠性和数据的安全性。展开更多
文摘针对物联网中传感网基础设施与应用程序分离将加重安全风险、认证机制不完善等问题,文中基于现存的互联网标准和数据包传输层安全协议DTLS(Datagram Transport Layer Security),提出了一个物联网环境下的端到端双向认证安全机制。该安全机制主要利用DTLS协议的全认证握手过程实现对用户和节点身份的双向认证,并对数据进行加密和完整性封装。该机制工作在标准低功耗通信栈上面,在部署了相关协议的传感节点上对安全机制的性能参数进行了评估,如传输延时、能量消耗、内存消耗等。验证其可行性,证明了该安全机制具有低开销和高互操作性。
基金funded by Researchers Supporting ProjectNumber(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
文摘The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.
文摘机车安全信息综合监测装置为机车众多车载监测设备提供了统一的机车安全信息。随着铁路智能化的深入发展,人们对机车安全信息综合监测装置数据的安全性要求日益提高。缺乏对机车安全信息综合监测装置有效的管控手段,会导致外围设备非法接入,造成通信总线被干扰。针对机车安全信息综合监测装置存在的问题,基于TAX18平台开展接入鉴权技术研究,提出了一套技术解决方案。在硬件上构建新的RS485通信总线架构,在软件上采用数据包传输层安全性协议(Datagram Transport Layer Security,DTLS)、mbedTLS等技术,实现机车安全信息综合监测装置相关外设的鉴权加密。应用的嵌入式领域加解密库具有较高的稳定性和可靠性,可从根本上解决外设接入问题,保障机车安全信息综合监测装置通信的可靠性和数据的安全性。
