Enhancing intrusion detection system using rectified linear unit function in pigeon inspired optimization algorithm

Agus Tedyyana, Osman Ghazali, Onno W. Purbo, M. A. A. Seman
{"title":"Enhancing intrusion detection system using rectified linear unit function in pigeon inspired optimization algorithm","authors":"Agus Tedyyana, Osman Ghazali, Onno W. Purbo, M. A. A. Seman","doi":"10.11591/ijai.v13.i2.pp1526-1534","DOIUrl":null,"url":null,"abstract":"The increasing rate of cybercrime in the digital world highlights the importance of having a reliable intrusion detection system (IDS) to detect unauthorized attacks and notify administrators. IDS can leverage machine learning techniques to identify patterns of attacks and provide real-time notifications. In building a successful IDS, selecting the right features is crucial as it determines the accuracy of the predictions made by the model. This paper presents a new IDS algorithm that combines the rectified linear unit (ReLU) activation function with a pigeon-inspired optimizer in feature selection. The proposed algorithm was evaluated on network security layer - knowledge discovery in databases (NSL-KDD) datasets and demonstrated improved performance in terms of training speed and accuracy compared to previous IDS models. Thus, the use of the ReLU activation function and a pigeon-inspired optimizer in feature selection can significantly enhance the effectiveness of an IDS in detecting unauthorized attacks.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence (IJ-AI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v13.i2.pp1526-1534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The increasing rate of cybercrime in the digital world highlights the importance of having a reliable intrusion detection system (IDS) to detect unauthorized attacks and notify administrators. IDS can leverage machine learning techniques to identify patterns of attacks and provide real-time notifications. In building a successful IDS, selecting the right features is crucial as it determines the accuracy of the predictions made by the model. This paper presents a new IDS algorithm that combines the rectified linear unit (ReLU) activation function with a pigeon-inspired optimizer in feature selection. The proposed algorithm was evaluated on network security layer - knowledge discovery in databases (NSL-KDD) datasets and demonstrated improved performance in terms of training speed and accuracy compared to previous IDS models. Thus, the use of the ReLU activation function and a pigeon-inspired optimizer in feature selection can significantly enhance the effectiveness of an IDS in detecting unauthorized attacks.
利用鸽子启发优化算法中的整流线性单元函数增强入侵检测系统
数字世界中的网络犯罪率不断上升,这凸显了拥有一个可靠的入侵检测系统(IDS)来检测未经授权的攻击并通知管理员的重要性。IDS 可以利用机器学习技术来识别攻击模式并提供实时通知。在构建成功的 IDS 时,选择正确的特征至关重要,因为它决定了模型预测的准确性。本文提出了一种新的 IDS 算法,该算法在特征选择中结合了整流线性单元(ReLU)激活函数和鸽子启发优化器。在网络安全层--数据库知识发现(NSL-KDD)数据集上对所提出的算法进行了评估,结果表明,与以前的 IDS 模型相比,该算法在训练速度和准确性方面都有了很大提高。因此,在特征选择中使用 ReLU 激活函数和鸽子启发优化器可以显著提高 IDS 检测未经授权攻击的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:481959085
Book学术官方微信