Intrusion Detection in Cloud Environment via Soft-Max Deep Spectral Recurrent Neural Network

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Sandanakaruppan Ammavasai, Hariharan Subramani, Manjunathan Alagarsamy, Sanmugavalli Palanisamy, Menaga Devendran, Sharon Priya Surendran
{"title":"Intrusion Detection in Cloud Environment via Soft-Max Deep Spectral Recurrent Neural Network","authors":"Sandanakaruppan Ammavasai,&nbsp;Hariharan Subramani,&nbsp;Manjunathan Alagarsamy,&nbsp;Sanmugavalli Palanisamy,&nbsp;Menaga Devendran,&nbsp;Sharon Priya Surendran","doi":"10.1002/cpe.70161","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Cloud computing is one of the most promising technologies for effectively storing data and offering internet services. There are several benefits to using this quickly evolving technology instead of more conventional defenses to shield computer-based systems from cyberattacks. In this paper, a novel Intrusion Detection in Cloud Environment via Soft-max Deep Spectral Recurrent Neural Network has been proposed to improve the security in cloud computing. Initially, Data preprocessing using IoT-23 dataset values reduces null or inappropriate feature values. Feature extraction utilizes Principal Component Analysis (PCA) to reduce dimensionality while retaining significant information. Feature selection is optimized using the Reptile Search algorithm (RSO) to prioritize relevant features by evaluating their relational weights. A Soft-max Deep Spectral Recurrent Neural Network (SDSRN<sup>2</sup>) classifies data into intrusion or non-intrusion categories. Detected intrusions undergo further analysis using a Recursive Multi-Perception Neural Classifier (RMNC) to assess risk levels. To evaluate the effectiveness of the proposed model, several metrics are utilized, namely accuracy, precision, F1 score, and recall. The performance analysis of accuracy attained by the proposed technique is 99.5%, which is higher than the existing technique. The proposed approach compared to existing methods such as SSAFS-DLID, SeArch, Improved-IDs, and the proposed model improves detection accuracy by 5.18%, 3.7%, and 1.77%, respectively.</p>\n </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concurrency and Computation-Practice & Experience","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cpe.70161","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

Cloud computing is one of the most promising technologies for effectively storing data and offering internet services. There are several benefits to using this quickly evolving technology instead of more conventional defenses to shield computer-based systems from cyberattacks. In this paper, a novel Intrusion Detection in Cloud Environment via Soft-max Deep Spectral Recurrent Neural Network has been proposed to improve the security in cloud computing. Initially, Data preprocessing using IoT-23 dataset values reduces null or inappropriate feature values. Feature extraction utilizes Principal Component Analysis (PCA) to reduce dimensionality while retaining significant information. Feature selection is optimized using the Reptile Search algorithm (RSO) to prioritize relevant features by evaluating their relational weights. A Soft-max Deep Spectral Recurrent Neural Network (SDSRN2) classifies data into intrusion or non-intrusion categories. Detected intrusions undergo further analysis using a Recursive Multi-Perception Neural Classifier (RMNC) to assess risk levels. To evaluate the effectiveness of the proposed model, several metrics are utilized, namely accuracy, precision, F1 score, and recall. The performance analysis of accuracy attained by the proposed technique is 99.5%, which is higher than the existing technique. The proposed approach compared to existing methods such as SSAFS-DLID, SeArch, Improved-IDs, and the proposed model improves detection accuracy by 5.18%, 3.7%, and 1.77%, respectively.

基于Soft-Max深度频谱递归神经网络的云环境入侵检测
云计算是有效存储数据和提供互联网服务的最有前途的技术之一。使用这种快速发展的技术而不是更传统的防御来保护基于计算机的系统免受网络攻击有几个好处。为了提高云计算的安全性,本文提出了一种基于Soft-max深度频谱递归神经网络的云环境入侵检测方法。最初,使用IoT-23数据集值进行数据预处理可以减少空值或不合适的特征值。特征提取利用主成分分析(PCA)在保留重要信息的同时降低维数。使用爬虫搜索算法(RSO)优化特征选择,通过评估相关特征的关系权重来确定相关特征的优先级。SDSRN2 (Soft-max Deep Spectral Recurrent Neural Network)将数据分为入侵类和非入侵类。检测到的入侵将使用递归多感知神经分类器(RMNC)进行进一步分析,以评估风险水平。为了评估所提出模型的有效性,使用了几个指标,即准确性,精度,F1分数和召回率。该方法的性能分析精度为99.5%,高于现有方法。与现有的SSAFS-DLID、SeArch、Improved-IDs等方法相比,该方法的检测准确率分别提高了5.18%、3.7%和1.77%。
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
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
×
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