使用机器学习的电子健康记录监测无线通信的安全感知路由

Q2 Nursing
Sudhakar Sengan, O. Khalaf, G. Rao, D. Sharma, Amarendra K., A. A. Hamad
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引用次数: 41

摘要

特设结构是自组织、自形成和系统无关的,没有邻近的联系。我们在框架中必须关注的一个重要限制是领先性。至于方向,我们可以将数据包或通信从发送方发送到接收方节点。AODV路由协议,一个简短的显示,将使教程可按需提供。基于机器学习(ML)的入侵检测必须集成和完善,以支持漏洞检测,并使框架能够在ML涉及其移动环境时做出入侵决策。本文考虑了沿途弯道困难、冲击层中离地权问题、遥控偏离路线引发的包灾等因素的综合影响。本文采用AODV作为路由MANET协议,利用支持向量机(SVM)对该过程进行设计和评估,以检测恶意网络节点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Security-Aware Routing on Wireless Communication for E-Health Records Monitoring Using Machine Learning
An ad hoc structure is self-organizing, self-forming, and system-free, with no nearby associations. One of the significant limits we must focus on in frameworks is leading. As for directions, we can send the packet or communications from the sender to the recipient node. AODV Routing Protocol, a short display that will make the tutorial available on demand. Machine Learning (ML) based IDS must be integrated and perfected to support the detection of vulnerabilities and enable frameworks to make intrusion decisions while ML is about their mobile context. This paper considers the combined effect of stooped difficulties along the way, problems at the medium get-right-of-area to impact layer, or pack disasters triggered by the remote control going off route. The AODV as the Routing MANET protocol is used in this work, and the process is designed and evaluated using Support Vector Machine (SVM) to detect the malicious network nodes.
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来源期刊
CiteScore
3.20
自引率
0.00%
发文量
43
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