基于深度神经网络参数的入侵检测系统性能改进

Amin Rezaeipanah, Esmat Afsoon, Gholamreza Ahmadi
{"title":"基于深度神经网络参数的入侵检测系统性能改进","authors":"Amin Rezaeipanah, Esmat Afsoon, Gholamreza Ahmadi","doi":"10.1109/ICCKE50421.2020.9303701","DOIUrl":null,"url":null,"abstract":"One of the solutions used to create security in computer networks is the use of intrusion detection systems. Today’s architectures detecting intrusion have made it difficult for the designers of these systems to select an efficient architecture that can be more reliable in detecting intrusions. One of the solutions that has been developed to secure computer systems and networks is the emergence of intrusion detection systems. In the present study, a method for combining deep learning and observer learning in detecting intrusion patterns is presented with the aim of increasing the security of computer networks. Observer learning is provided to teach the parameters of a deep neural network algorithm that uses linear combinations and representations of effective features. This method is based on a learning algorithm with supervision and a deep neural network that optimizes the appropriate number of hidden layers and the number of neurons in each layer according to a threshold value. The results of experiments on NSL-KDD data set show the superiority of the proposed method with 97.64% accuracy over MARS and DLNN methods.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Improving the Performance of Intrusion Detection Systems Using the Development of Deep Neural Network Parameters\",\"authors\":\"Amin Rezaeipanah, Esmat Afsoon, Gholamreza Ahmadi\",\"doi\":\"10.1109/ICCKE50421.2020.9303701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the solutions used to create security in computer networks is the use of intrusion detection systems. Today’s architectures detecting intrusion have made it difficult for the designers of these systems to select an efficient architecture that can be more reliable in detecting intrusions. One of the solutions that has been developed to secure computer systems and networks is the emergence of intrusion detection systems. In the present study, a method for combining deep learning and observer learning in detecting intrusion patterns is presented with the aim of increasing the security of computer networks. Observer learning is provided to teach the parameters of a deep neural network algorithm that uses linear combinations and representations of effective features. This method is based on a learning algorithm with supervision and a deep neural network that optimizes the appropriate number of hidden layers and the number of neurons in each layer according to a threshold value. The results of experiments on NSL-KDD data set show the superiority of the proposed method with 97.64% accuracy over MARS and DLNN methods.\",\"PeriodicalId\":402043,\"journal\":{\"name\":\"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCKE50421.2020.9303701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCKE50421.2020.9303701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

用于在计算机网络中创建安全的解决方案之一是使用入侵检测系统。当前的入侵检测体系结构给系统设计者选择一种更可靠的入侵检测体系结构带来了困难。为了保护计算机系统和网络的安全,入侵检测系统的出现是已经发展起来的解决方案之一。为了提高计算机网络的安全性,提出了一种将深度学习与观察者学习相结合的入侵模式检测方法。提供观测器学习来教授深度神经网络算法的参数,该算法使用线性组合和有效特征的表示。该方法基于带监督的学习算法和深度神经网络,根据阈值优化适当的隐藏层数和每层神经元数。在NSL-KDD数据集上的实验结果表明,该方法比MARS和DLNN方法具有97.64%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the Performance of Intrusion Detection Systems Using the Development of Deep Neural Network Parameters
One of the solutions used to create security in computer networks is the use of intrusion detection systems. Today’s architectures detecting intrusion have made it difficult for the designers of these systems to select an efficient architecture that can be more reliable in detecting intrusions. One of the solutions that has been developed to secure computer systems and networks is the emergence of intrusion detection systems. In the present study, a method for combining deep learning and observer learning in detecting intrusion patterns is presented with the aim of increasing the security of computer networks. Observer learning is provided to teach the parameters of a deep neural network algorithm that uses linear combinations and representations of effective features. This method is based on a learning algorithm with supervision and a deep neural network that optimizes the appropriate number of hidden layers and the number of neurons in each layer according to a threshold value. The results of experiments on NSL-KDD data set show the superiority of the proposed method with 97.64% accuracy over MARS and DLNN methods.
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
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信