基于深度学习的网络入侵检测系统优化
摘 要
本研究旨在基于深度学习技术优化网络入侵检测系统，以提升其性能和适应能力。具体而言，研究首先分析了当前主流深度学习模型在网络入侵检测中的适用性，并提出了一种融合卷积神经网络（CNN）与长短时记忆网络（LSTM）的混合架构，该架构能够同时提取数据的空间特征和时间序列特征，从而更好地捕捉网络流量中的潜在攻击模式。此外，为解决入侵检测数据集不平衡的问题，引入了改进的过采样算法，有效提升了模型对少数类攻击的识别能力。实验部分采用NSL-KDD和CICIDS2017两个标准数据集进行验证，结果表明，所提出的模型在检测准确率、召回率及F1分数等方面均显著优于传统方法和其他单一深度学习模型。研究表明，通过结合多类型深度学习网络并针对性地优化数据处理流程，可以显著增强入侵检测系统的综合性能，为实际应用提供了可行的技术路径。
关键词：网络入侵检测 深度学习 CNN-LSTM混合架构
Abstract
This study aims to optimize the network intrusion detection system based on deep learning techniques to improve its performance and adaptability. Specifically, the study first analyzes the current mainstream deep learning model in the applicability of network intrusion detection, and puts forward a fusion convolutional neural network (CNN) and long short memory network (LSTM) hybrid architecture, the architecture can extract the data space and the time sequence features, so as to better capture the potential attack patterns in the network traffic. In addition, in order to solve the problem of imbalance of intrusion detection data sets, an improved oversampling algorithm is introduced, which effectively improves the recognition ability of the model to the minority types of attacks. In the experimental part, two standard datasets NSL-KDD and CICIDS2017 were used for verification, and the proposed model significantly outperforms traditional methods and other single deep learning models in terms of detection accuracy, recall rate and F1 score. The study shows that by combining multi-type deep learning network and optimizing the data processing process, the comprehensive performance of intrusion detection system can be significantly enhanced, providing a feasible technical path for practical application.
Keyword:Network Intrusion Detection Deep Learning Cnn-Lstm Hybrid Architecture
目 录
引言 1
1深度学习技术概述 1
1.1深度学习基础理论 1
1.2网络入侵检测中的深度学习应用现状 2
1.3主流深度学习模型对比分析 2
2网络入侵检测系统需求分析 3
2.1入侵检测系统的功能与目标 3
2.2当前网络入侵检测系统的局限性 3
2.3深度学习对入侵检测的潜在提升点 4
2.4数据集选择与预处理的重要性 4
2.5性能评估指标体系构建 4
3基于深度学习的优化方法研究 5
3.1优化算法的选择与设计 5
3.2特征提取与表示学习优化 5
3.3模型训练过程中的效率改进 6
3.4泛化能力与鲁棒性增强策略 6
3.5资源消耗与实时性平衡 6
4实验验证与结果分析 7
4.1实验环境与数据集描述 7
4.2不同优化方法的对比实验 7
4.3性能指标的详细分析 8
4.4误报率与检测率的权衡探讨 8
结论 8
参考文献 10
致谢 11
摘 要
本研究旨在基于深度学习技术优化网络入侵检测系统，以提升其性能和适应能力。具体而言，研究首先分析了当前主流深度学习模型在网络入侵检测中的适用性，并提出了一种融合卷积神经网络（CNN）与长短时记忆网络（LSTM）的混合架构，该架构能够同时提取数据的空间特征和时间序列特征，从而更好地捕捉网络流量中的潜在攻击模式。此外，为解决入侵检测数据集不平衡的问题，引入了改进的过采样算法，有效提升了模型对少数类攻击的识别能力。实验部分采用NSL-KDD和CICIDS2017两个标准数据集进行验证，结果表明，所提出的模型在检测准确率、召回率及F1分数等方面均显著优于传统方法和其他单一深度学习模型。研究表明，通过结合多类型深度学习网络并针对性地优化数据处理流程，可以显著增强入侵检测系统的综合性能，为实际应用提供了可行的技术路径。
关键词：网络入侵检测 深度学习 CNN-LSTM混合架构
Abstract
This study aims to optimize the network intrusion detection system based on deep learning techniques to improve its performance and adaptability. Specifically, the study first analyzes the current mainstream deep learning model in the applicability of network intrusion detection, and puts forward a fusion convolutional neural network (CNN) and long short memory network (LSTM) hybrid architecture, the architecture can extract the data space and the time sequence features, so as to better capture the potential attack patterns in the network traffic. In addition, in order to solve the problem of imbalance of intrusion detection data sets, an improved oversampling algorithm is introduced, which effectively improves the recognition ability of the model to the minority types of attacks. In the experimental part, two standard datasets NSL-KDD and CICIDS2017 were used for verification, and the proposed model significantly outperforms traditional methods and other single deep learning models in terms of detection accuracy, recall rate and F1 score. The study shows that by combining multi-type deep learning network and optimizing the data processing process, the comprehensive performance of intrusion detection system can be significantly enhanced, providing a feasible technical path for practical application.
Keyword:Network Intrusion Detection Deep Learning Cnn-Lstm Hybrid Architecture
目 录
引言 1
1深度学习技术概述 1
1.1深度学习基础理论 1
1.2网络入侵检测中的深度学习应用现状 2
1.3主流深度学习模型对比分析 2
2网络入侵检测系统需求分析 3
2.1入侵检测系统的功能与目标 3
2.2当前网络入侵检测系统的局限性 3
2.3深度学习对入侵检测的潜在提升点 4
2.4数据集选择与预处理的重要性 4
2.5性能评估指标体系构建 4
3基于深度学习的优化方法研究 5
3.1优化算法的选择与设计 5
3.2特征提取与表示学习优化 5
3.3模型训练过程中的效率改进 6
3.4泛化能力与鲁棒性增强策略 6
3.5资源消耗与实时性平衡 6
4实验验证与结果分析 7
4.1实验环境与数据集描述 7
4.2不同优化方法的对比实验 7
4.3性能指标的详细分析 8
4.4误报率与检测率的权衡探讨 8
结论 8
参考文献 10
致谢 11
