使用 LZ-JC-DBSCAN、EPRC-RPOA 和 EG-GELU-GRU 的增强型双重入侵检测系统的创新模型

IF 1.5 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Jeyavim Sherin R. C., Parkavi K.
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引用次数: 0

摘要

随着互联网访问量的增加,网络通信中的可疑活动日益增多,因此有必要开发先进的入侵检测系统(IDS)。现有的 IDS 解决方案在检测可疑活动方面往往表现不佳,而且无法识别监控网络流量的数据包捕获(PCAP)文件中的各种攻击类型。本文提出了一种基于深度学习的双 IDS 模型,旨在解决这些问题。首先,利用 CSE-CIC-IDS2019 数据集从 PCAP 文件中提取特征。使用指数几何-高斯误差线性单元-门控递归单元(EG-GELU-GRU)方法检测可疑活动。正常数据通过基于对数 ZScore-Jacosine 密度的带噪声应用空间聚类(LZ-JC-DBSCAN)进行进一步的特征提取和预处理。使用熵皮尔逊 R 相关性-红熊猫优化算法对特征选择进行优化。对可疑文件进行标记,同时对正常数据进行负载平衡。通过使用 Glorot Kaufman 双向编码器表示变换器技术进行字嵌入,并通过 EG-GELU-GRU 模型进行分类,实现了攻击检测。受攻击的数据包会被阻止,然后重新应用该方法进行攻击类型分类。使用 Python 进行的实验结果表明,该模型性能优越,准确率达到 98.18%,精确率达到 98.73%,超越了现有方法,显著增强了入侵检测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An innovative model for an enhanced dual intrusion detection system using LZ-JC-DBSCAN, EPRC-RPOA and EG-GELU-GRU

An innovative model for an enhanced dual intrusion detection system using LZ-JC-DBSCAN, EPRC-RPOA and EG-GELU-GRU

The rise of suspicious activities in network communication, driven by increased internet accessibility, necessitates the development of advanced intrusion detection systems (IDS). Existing IDS solutions often exhibit poor performance in detecting suspicious activity and fail to identify various attack types within packet capture (PCAP) files, which monitor network traffic. This paper proposes a deep learning-based dual IDS model designed to address these issues. The process begins with utilizing the CSE-CIC-IDS2019 dataset to extract features from PCAP files. Suspicious activities are detected using the Exponential Geometric-Gaussian Error Linear Units-Gated Recurrent Unit (EG-GELU-GRU) method. Normal data undergoes further feature extraction and preprocessing through Log ZScore-Jacosine Density-Based Spatial Clustering of Applications with Noise (LZ-JC-DBSCAN). Feature selection is optimized using the Entropy Pearson R Correlation-Red Panda optimization algorithm. Suspicious files are flagged, while load balancing is performed on normal data. Attack detection is achieved through word embedding with the Glorot Kaufman-bidirectional encoder representations from transformers technique and classification via the EG-GELU-GRU model. Attacked packets are blocked, and the method is reapplied for attack-type classification. Experimental results using Python demonstrate the model’s superior performance, achieving 98.18% accuracy and 98.73% precision, surpassing existing approaches and significantly enhancing intrusion detection capabilities.

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来源期刊
IET Communications
IET Communications 工程技术-工程:电子与电气
CiteScore
4.30
自引率
6.20%
发文量
220
审稿时长
5.9 months
期刊介绍: IET Communications covers the fundamental and generic research for a better understanding of communication technologies to harness the signals for better performing communication systems using various wired and/or wireless media. This Journal is particularly interested in research papers reporting novel solutions to the dominating problems of noise, interference, timing and errors for reduction systems deficiencies such as wasting scarce resources such as spectra, energy and bandwidth. Topics include, but are not limited to: Coding and Communication Theory; Modulation and Signal Design; Wired, Wireless and Optical Communication; Communication System Special Issues. Current Call for Papers: Cognitive and AI-enabled Wireless and Mobile - https://digital-library.theiet.org/files/IET_COM_CFP_CAWM.pdf UAV-Enabled Mobile Edge Computing - https://digital-library.theiet.org/files/IET_COM_CFP_UAV.pdf
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