一个使用混合多层深度学习模型的网络入侵检测系统。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
ACS Applied Bio Materials Pub Date : 2024-10-01 Epub Date: 2022-06-14 DOI:10.1089/big.2021.0268
Muhammad Basit Umair, Zeshan Iqbal, Muhammad Ahmad Faraz, Muhammad Attique Khan, Yu-Dong Zhang, Navid Razmjooy, Sefedine Kadry
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引用次数: 0

摘要

入侵检测系统(IDS)旨在检测和分析网络流量中的可疑活动。在IDSs的文献中介绍了几种方法;然而,由于数据量大,这些模型未能达到较高的精度。针对传统入侵检测方法检测结果不理想的问题,本文提出了一种统计方法。使用多层卷积神经网络提取和选择特征,并使用softmax分类器对网络入侵进行分类。为了进行进一步的分析,还将多层深度神经网络应用于网络入侵分类。此外,实验使用了两个常用的基准入侵检测数据集:NSL-KDD和KDDCUP’99。使用四个性能指标来评估所提出模型的性能:准确性、召回率、F1分数和精确度。实验结果表明,与其他IDS相比,所提出的方法获得了更好的精度(99%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model.

An intrusion detection system (IDS) is designed to detect and analyze network traffic for suspicious activity. Several methods have been introduced in the literature for IDSs; however, due to a large amount of data, these models have failed to achieve high accuracy. A statistical approach is proposed in this research due to the unsatisfactory results of traditional intrusion detection methods. The features are extracted and selected using a multilayer convolutional neural network, and a softmax classifier is employed to classify the network intrusions. To perform further analysis, a multilayer deep neural network is also applied to classify network intrusions. Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed model is evaluated using four performance metrics: accuracy, recall, F1-score, and precision. The experimental results show that the proposed approach achieved better accuracy (99%) compared with other IDSs.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
期刊介绍: ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.
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