一种高效的移动自组网神经深度学习入侵检测系统

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-04-04 DOI:10.4108/eai.4-4-2022.173781
N. Venkateswaran, S. Prabaharan
{"title":"一种高效的移动自组网神经深度学习入侵检测系统","authors":"N. Venkateswaran, S. Prabaharan","doi":"10.4108/eai.4-4-2022.173781","DOIUrl":null,"url":null,"abstract":"As of late mobile ad hoc networks (MANETs) have turned into a very popular explore the theme. By giving interchanges without a fixed infrastructure MANETs are an appealing innovation for some applications, for ex, reassigning tasks, strategic activities, nature observing, meetings, & so forth. This paper proposes the use of a neuro Deep learning wireless intrusion detection system that distinguishes the attacks in MANETs. Executing security is a hard task in MANET due to its immutable vulnerabilities. Deep learning gives extra security to such systems and the proposed framework comprises a hybrid conspiracy that joins the determination and abnormality-based methodologies. Executing the partial IDS utilizing neuro Deep learning improves the identification rate in MANETs. The proposed plan utilizes deep neural networks and a cross breed neural system. It demonstrates that Recurrent neural networks can successfully improve the identification and diminish the rate of false caution and failure.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"9 1","pages":"e7"},"PeriodicalIF":1.1000,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks\",\"authors\":\"N. Venkateswaran, S. Prabaharan\",\"doi\":\"10.4108/eai.4-4-2022.173781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As of late mobile ad hoc networks (MANETs) have turned into a very popular explore the theme. By giving interchanges without a fixed infrastructure MANETs are an appealing innovation for some applications, for ex, reassigning tasks, strategic activities, nature observing, meetings, & so forth. This paper proposes the use of a neuro Deep learning wireless intrusion detection system that distinguishes the attacks in MANETs. Executing security is a hard task in MANET due to its immutable vulnerabilities. Deep learning gives extra security to such systems and the proposed framework comprises a hybrid conspiracy that joins the determination and abnormality-based methodologies. Executing the partial IDS utilizing neuro Deep learning improves the identification rate in MANETs. The proposed plan utilizes deep neural networks and a cross breed neural system. It demonstrates that Recurrent neural networks can successfully improve the identification and diminish the rate of false caution and failure.\",\"PeriodicalId\":43034,\"journal\":{\"name\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"volume\":\"9 1\",\"pages\":\"e7\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAI Endorsed Transactions on Scalable Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/eai.4-4-2022.173781\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Scalable Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eai.4-4-2022.173781","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

最近,移动自组织网络(manet)已经变成了一个非常流行的探索主题。通过在没有固定基础设施的情况下提供交换,manet对于一些应用来说是一个有吸引力的创新,例如,重新分配任务,战略活动,自然观察,会议等等。本文提出了一种基于神经深度学习的无线入侵检测系统,用于识别无线网络中的攻击。由于其不可变的漏洞,在MANET中执行安全是一项艰巨的任务。深度学习为这样的系统提供了额外的安全性,所提出的框架包括一个混合阴谋,它结合了确定和基于异常的方法。利用神经深度学习来执行部分IDS,可以提高自适应神经网络的识别率。该方案利用深度神经网络和杂交神经系统。结果表明,递归神经网络可以有效地提高识别效率,降低误报率和失败率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks
As of late mobile ad hoc networks (MANETs) have turned into a very popular explore the theme. By giving interchanges without a fixed infrastructure MANETs are an appealing innovation for some applications, for ex, reassigning tasks, strategic activities, nature observing, meetings, & so forth. This paper proposes the use of a neuro Deep learning wireless intrusion detection system that distinguishes the attacks in MANETs. Executing security is a hard task in MANET due to its immutable vulnerabilities. Deep learning gives extra security to such systems and the proposed framework comprises a hybrid conspiracy that joins the determination and abnormality-based methodologies. Executing the partial IDS utilizing neuro Deep learning improves the identification rate in MANETs. The proposed plan utilizes deep neural networks and a cross breed neural system. It demonstrates that Recurrent neural networks can successfully improve the identification and diminish the rate of false caution and failure.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
期刊最新文献
Factors influencing the employment intention of private college graduates based on robot control system design Japanese Flipped Classroom Knowledge Acquisition Based on Canvas Web-Based Learning Management System Effectiveness and perception of augmented reality in the teaching of structured programming fundamentals in university students Mechanical Design Method and Joint Simulation Analysis of Industrial Robots Based on Trajectory Planning Algorithm and Kinematics Global research on ubiquitous learning: A network and output approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1