基于深度学习的网络入侵检测系统优化
摘 要
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统网络入侵检测系统在应对新型攻击时面临诸多挑战。为此，本研究旨在构建基于深度学习的网络入侵检测系统优化方案，以提高检测精度和实时性。研究采用卷积神经网络与长短期记忆网络相结合的方法，针对网络流量数据特征提取困难的问题，提出一种融合多尺度特征提取机制的改进模型。该模型通过引入注意力机制，有效增强了对关键特征的关注度，提升了模型的学习能力。实验结果表明，在KDD99、NSL - KDD等多个标准数据集上，所提方法相较于传统机器学习算法及单一深度学习模型，检测率分别提高了15% - 20%，误报率降低了8% - 12%。此外，为解决模型训练时间过长的问题，引入迁移学习思想，利用预训练模型参数进行初始化，大幅缩短了训练周期。
关键词：网络入侵检测 深度学习 多尺度特征提取
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional network intrusion detection system faces many challenges when dealing with new attacks. To this end, this study aims to construct a deep learning-based network intrusion detection system optimization scheme to improve detection accuracy and real-time performance. This paper combines convolutional neural network with long and short-term memory network to propose a modified model integrating the difficulty of feature extraction of network traffic data. By introducing the attention mechanism, the model effectively enhances the attention to the key features and improves the learning ability of the model. The experimental results show that compared with the traditional machine learning algorithm and Ne-deep learning model, the detection rate increases by 15% -20% and the false positive rate decreases by 8% -12% respectively on the standard data sets such as KDD 99 and NSL-KDD. In addition, in order to solve the problem of too long model training time, the transfer learning idea is introduced, and the pre-training model parameters are used for initialization, which greatly shortens the training period.
Keyword:Network Intrusion Detection Deep Learning Multi-scale Feature Extraction
目 录
1绪论 1
1.1网络入侵检测系统优化背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2深度学习模型选择与优化 2
2.1入侵检测常用深度学习模型 2
2.2模型性能评估指标体系 3
2.3针对入侵检测的模型改进 3
3数据预处理与特征提取优化 4
3.1原始数据获取与清洗 4
3.2特征选择与降维技术 4
3.3特征工程在入侵检测中的应用 5
4系统架构设计与性能提升 6
4.1分布式架构设计思路 6
4.2实时检测机制优化 6
4.3性能瓶颈分析与解决 7
结论 7
参考文献 9
致谢 10
摘 要
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统网络入侵检测系统在应对新型攻击时面临诸多挑战。为此，本研究旨在构建基于深度学习的网络入侵检测系统优化方案，以提高检测精度和实时性。研究采用卷积神经网络与长短期记忆网络相结合的方法，针对网络流量数据特征提取困难的问题，提出一种融合多尺度特征提取机制的改进模型。该模型通过引入注意力机制，有效增强了对关键特征的关注度，提升了模型的学习能力。实验结果表明，在KDD99、NSL - KDD等多个标准数据集上，所提方法相较于传统机器学习算法及单一深度学习模型，检测率分别提高了15% - 20%，误报率降低了8% - 12%。此外，为解决模型训练时间过长的问题，引入迁移学习思想，利用预训练模型参数进行初始化，大幅缩短了训练周期。
关键词：网络入侵检测 深度学习 多尺度特征提取
Abstract
With the rapid development of information technology, the network security threats are increasingly complex and changeable, and the traditional network intrusion detection system faces many challenges when dealing with new attacks. To this end, this study aims to construct a deep learning-based network intrusion detection system optimization scheme to improve detection accuracy and real-time performance. This paper combines convolutional neural network with long and short-term memory network to propose a modified model integrating the difficulty of feature extraction of network traffic data. By introducing the attention mechanism, the model effectively enhances the attention to the key features and improves the learning ability of the model. The experimental results show that compared with the traditional machine learning algorithm and Ne-deep learning model, the detection rate increases by 15% -20% and the false positive rate decreases by 8% -12% respectively on the standard data sets such as KDD 99 and NSL-KDD. In addition, in order to solve the problem of too long model training time, the transfer learning idea is introduced, and the pre-training model parameters are used for initialization, which greatly shortens the training period.
Keyword:Network Intrusion Detection Deep Learning Multi-scale Feature Extraction
目 录
1绪论 1
1.1网络入侵检测系统优化背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2深度学习模型选择与优化 2
2.1入侵检测常用深度学习模型 2
2.2模型性能评估指标体系 3
2.3针对入侵检测的模型改进 3
3数据预处理与特征提取优化 4
3.1原始数据获取与清洗 4
3.2特征选择与降维技术 4
3.3特征工程在入侵检测中的应用 5
4系统架构设计与性能提升 6
4.1分布式架构设计思路 6
4.2实时检测机制优化 6
4.3性能瓶颈分析与解决 7
结论 7
参考文献 9
致谢 10
