鲁棒和安全5G网络的机器学习方法分析

Piyush Kulshreshtha, A. Garg
{"title":"鲁棒和安全5G网络的机器学习方法分析","authors":"Piyush Kulshreshtha, A. Garg","doi":"10.1109/ICEEICT53079.2022.9768557","DOIUrl":null,"url":null,"abstract":"The 5G Network provides higher bandwidth, low latency, low TCO and an ultra density network through use of several new technologies. However, these technologies also lead to a lot of vulnerabilities in the network and make it susceptible to security attacks by hackers. Detection of these attacks requires anomaly detection in network traffic which can be done quickly and efficiently through machine learning techniques. This review paper explores the use of several such supervised learning techniques for Intrusion Detection. A popular dataset _ KDD99, has been utilized to model and compare Intrusion Detection through a set of multi class classifiers. The dataset was cleaned and processed to remove the features that showed very high correlation with each other, The classifier used are Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Support Vector Machine and Gradient Boost. The paper also compares the performance of these classifiers for detecting abnormal traffic patterns in KDD99 dataset.","PeriodicalId":201910,"journal":{"name":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of Machine Learning Approaches for Robust and Secure 5G Networks\",\"authors\":\"Piyush Kulshreshtha, A. Garg\",\"doi\":\"10.1109/ICEEICT53079.2022.9768557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The 5G Network provides higher bandwidth, low latency, low TCO and an ultra density network through use of several new technologies. However, these technologies also lead to a lot of vulnerabilities in the network and make it susceptible to security attacks by hackers. Detection of these attacks requires anomaly detection in network traffic which can be done quickly and efficiently through machine learning techniques. This review paper explores the use of several such supervised learning techniques for Intrusion Detection. A popular dataset _ KDD99, has been utilized to model and compare Intrusion Detection through a set of multi class classifiers. The dataset was cleaned and processed to remove the features that showed very high correlation with each other, The classifier used are Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Support Vector Machine and Gradient Boost. The paper also compares the performance of these classifiers for detecting abnormal traffic patterns in KDD99 dataset.\",\"PeriodicalId\":201910,\"journal\":{\"name\":\"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEICT53079.2022.9768557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEICT53079.2022.9768557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

5G网络通过使用几种新技术提供更高的带宽、低延迟、低TCO和超密度网络。然而,这些技术也导致网络存在很多漏洞,容易受到黑客的安全攻击。检测这些攻击需要对网络流量进行异常检测,通过机器学习技术可以快速有效地完成异常检测。这篇综述文章探讨了入侵检测中几种监督学习技术的使用。利用一个流行的数据集KDD99,通过一组多类分类器对入侵检测进行建模和比较。对数据集进行清理和处理,去除相互之间相关性非常高的特征,使用的分类器是Naïve贝叶斯,决策树,逻辑回归,随机森林,支持向量机和梯度增强。本文还比较了这些分类器在KDD99数据集中检测异常流量模式的性能。
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
Analysis of Machine Learning Approaches for Robust and Secure 5G Networks
The 5G Network provides higher bandwidth, low latency, low TCO and an ultra density network through use of several new technologies. However, these technologies also lead to a lot of vulnerabilities in the network and make it susceptible to security attacks by hackers. Detection of these attacks requires anomaly detection in network traffic which can be done quickly and efficiently through machine learning techniques. This review paper explores the use of several such supervised learning techniques for Intrusion Detection. A popular dataset _ KDD99, has been utilized to model and compare Intrusion Detection through a set of multi class classifiers. The dataset was cleaned and processed to remove the features that showed very high correlation with each other, The classifier used are Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Support Vector Machine and Gradient Boost. The paper also compares the performance of these classifiers for detecting abnormal traffic patterns in KDD99 dataset.
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
期刊最新文献
Packet Transmission using Radio Access Protocol for Intra-Cluster Communications in Mobile Ad hoc Networks Performance of Combined RF and non-RF based Energy Harvesting scheme for Multi-Relay Cooperative Cognitive Radio Network Image Recognition, Classification and Analysis Using Convolutional Neural Networks An Optimized technique for a Sapid Motor pooling Tariff Forecasting System Pneumothorax Segmentation from Chest X-Rays Using U-Net/U-Net++ Architectures
×
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