检索结果-内蒙古大学图书馆

gradient scaling and segmented SoftMax Regression Federated Learning (GDS-SRFFL): a novel methodology for attack detection in industrial internet of things (IIoT) networks

引用

JOURNAL OF SUPERCOMPUTING 2024年第12期80卷 16860-16886页

作者： Rajasekaran, Vijay Anand Indirajithu, Alagiri Jayalakshmi, P. Nayyar, Anand Balusamy, Balamurugan Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 India Duy Tan Univ Fac Informat Technol Grad Sch Da Nang 550000 Vietnam Shiv Nadar Inst Eminence Greater Noida 201314 Uttar Pradesh India

Industrial internet of things (IIoT) is considered as large-scale IoT-based network comprising of sensors, communication channels, and security protocols used in Industry 4.0 for diverse real-time operations. Industrial IoT (IIoT) networks are vulnerable to diverse cyber threats and attacks. Attack detection is the biggest security issue in the IIoT. Various traditional attack detection methods are proposed by several researchers but all are insufficient to protect privacy and security. To address the issue, a novel gradient descent Scaling and Segmented Regression Fine-tuned Federated Learning (GDS-SRFFL) method is introduced for IIoT network attack detection. The aim of the GDS-SRFFL method is to enhance the security of an IIoT network. Initially, the novelty of gradient descent Scaling-based preprocessing is applied to the raw dataset for obtaining feature feature-scaled preprocessed network sample. Then, the unwanted intrusions are discovered by using a Segmented Regression Fine-tuned Mini-batch Federated Learning model to ensure the protection of IoT networks with the novelty of SoftMax Regression. In order to validate the proposed methodology, experimentations were conducted on different parameters, namely accuracy, precision, recall, specificity, and attack detection time, and the results concluded that proposed GDS-SRFFL has improved accuracy by 10%, precision by 13%, recall by 10%, specificity by 11% as well as minimum attack detection time by 28% as compared to existing techniques like CNN + LSTM (Altunay and Albayrak in Eng Sci Technol Int J 38:101322, 2023, https://***/10.1016/***.2022.101322), Enhanced Deep and Ensemble learning in SCADA-based IIoT network (Khan et al. in IEEE Trans Ind Inf 19(1):1030-1038, https://***/10.1109/TII.2022.3190352), RNN (Ullah and Mahmoud in IEEE Access 10:62722-62750, 2022, https://***/10.1109/ACCESS.2022.3176317), and other CNN methods. The proposed method "GDS-SRFFL" has overall accuracy of 89.42% as com

关键词： IIoT gradient descent function Feature scaling Segmented Regression Federated Learning

来源：评论

学校读者我要写书评

暂无评论

Super resolution of Astronomical Objects using Back Propagation Algorithm

Super resolution of Astronomical Objects using Back Propagat...

引用

IEEE International Conference on Inventive Computation Technologies (ICICT)

作者： Mhatre, Heena Bhosale, Varsha Vidyalankar Inst Technol Dept Informat Technol Bombay Maharashtra India

ISBN: (纸本)9781509012855

In the past years modern arithmetical methods for image investigation have led to a rebellion in many fields, from computer vision to scientific imaging. Though, some recently developed image processing techniques successfully oppressed by other sections have been infrequently, if ever, experimented on celestial observations. Here we present a new idea of super resolution of Astronomical objects using Back propagation algorithm."Super-resolution " is efficient in improving the excellence of analysis of diffused sources formerly unobserved by the background noise, efficiently rising the depth of obtainable observations. Higher-resolution image out of a set of low resolution frames can be obtained through super-resolution. Super-resolution is viable only for point sources which have negligible dimensions, then for wide-ranging objects the knowledge about intensity vacillation at angular prevalence is irreversibly mislaid. Again obtaining super resolved image for extended sources(e.g. comets, meteoroids, etc) is a new challenge if the speed of the object is very high. Acquiring High resolution images of celestial objects from ground based telescopes is intricate and often requires computational post processing techniques to remove blur caused by atmospheric commotion. Even images obtained through satellite imaging are compressed and sent to earth. So there is need for Super resolution of those compressed or noisy images. So, here we simply implement Super-resolution for Astronomical objects using Back propagation algorithm to overcome lost information and challenges for high speedy celestial objects. The purpose is to super resolve high speedy celestial objects whose analysis may in future help to prevent collisions of such celestial objects with earth and also avoid future solar system damage

关键词： Super resolution Astronomical observations Back propagation algorithm Low resolution images gradient descent function mean square error function Gaussian kernel Linear Kernel Blur Sigma factor sparse toeplitz matrix bicubic interpolation High resolution image

来源：评论

学校读者我要写书评

暂无评论

Super resolution of Astronomical Objects using Back Propagation Algorithm.

Super resolution of Astronomical Objects using Back Propagat...

引用

International Conference on Inventive Computation Technologies

作者： Heena Mhatre Varsha Bhosale Department of Information Technology Vidyalankar Institute of Technology Mumbai India

ISBN: (纸本)9781509012862

In the past years modern arithmetical methods for image investigation have led to a rebellion in many fields, from computer vision to scientific imaging. Though, some recently developed image processing techniques successfully oppressed by other sections have been infrequently, if ever, experimented on celestial observations. Here we present a new idea of super resolution of Astronomical objects using Back propagation algorithm."Super-resolution "is efficient in improving the excellence of analysis of diffused sources formerly unobserved by the background noise, efficiently rising the depth of obtainable observations. Higher-resolution image out of a set of low resolution frames can be obtained through super-resolution. Super-resolution is viable only for point sources which have negligible dimensions, then for wideranging objects the knowledge about intensity vacillation at angular prevalence is irreversibly mislaid. Again obtaining super resolved image for extended sources(e.g. comets, meteoroids, etc) is a new challenge if the speed of the object is very high. Acquiring High resolution images of celestial objects from ground based telescopes is intricate and often requires computational post processing techniques to remove blur caused by atmospheric commotion. Even images obtained through satellite imaging are compressed and sent to earth. So there is need for Super resolution of those compressed or noisy images. So, here we simply implement Super-resolution for Astronomical objects using Back propagation algorithm to overcome lost information and challenges for high speedy celestial objects. The purpose is to super resolve high speedy celestial objects whose analysis may in future help to prevent collisions of such celestial objects with earth and also avoid future solar system damage

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：