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，传统基于规则和特征匹配的安全防护手段逐渐难以应对新型攻击。为此，本研究旨在构建一种基于深度学习的网络安全威胁预测模型，以实现对潜在安全风险的高效识别与预警。该研究利用深度神经网络强大的非线性映射能力，结合网络安全领域的特点，选取了包括流量数据、日志信息等在内的多源异构数据作为输入特征，并采用长短期记忆网络（LSTM）来捕捉时间序列中的依赖关系，同时引入注意力机制以增强模型对关键特征的关注度。通过在公开数据集及实际企业内网环境中进行实验验证，结果表明所提模型能够有效检测出多种类型的网络攻击行为，如DDoS攻击、恶意软件传播等，其准确率达到95%以上，且误报率低于5%，相较于传统方法具有显著优势。
关键词：深度学习 网络安全威胁预测 长短期记忆网络
Abstract
With the rapid development of information technology, network security threats are increasingly complex and changeable, and the traditional security protection means based on rules and features matching are gradually difficult to deal with new attacks. To this end, this study aims to construct a deep learning-based cybersecurity threat prediction model to achieve efficient identification and early warning of potential security risks. The study using the depth of neural network powerful nonlinear mapping ability, combined with the characteristics of the field of network security, selected, including traffic data, log information, heterogeneous data as input features, and the long and short-term memory network (LSTM) to capture the dependence in the time series, introduce attention mechanism at the same time to enhance the model of key features. Through the experimental verification in the public data set and the actual enterprise Intranet environment, the results show that the proposed model can effectively detect various types of network attacks, such as DDoS attack, malicious software propagation, etc., the accuracy is more than 95%, and the false alarm rate is less than 5%, which has significant advantages compared with traditional methods.
Keyword:Deep Learning Cybersecurity Threat Prediction Long Short-Term Memory Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 2
2深度学习算法选择与优化 2
2.1常见深度学习算法分析 2
2.2算法性能评估指标 3
2.3针对网络安全的算法优化 3
3网络安全威胁数据处理 4
3.1数据来源与采集方式 4
3.2数据预处理技术 4
3.3特征提取与选择 5
4模型设计与实现 6
4.1模型架构设计 6
4.2关键技术实现 7
4.3模型训练与调优 7
结论 8
参考文献 10
致谢 11
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统基于规则和特征匹配的安全防护手段逐渐难以应对新型攻击。为此，本研究旨在构建一种基于深度学习的网络安全威胁预测模型，以实现对潜在安全风险的高效识别与预警。该研究利用深度神经网络强大的非线性映射能力，结合网络安全领域的特点，选取了包括流量数据、日志信息等在内的多源异构数据作为输入特征，并采用长短期记忆网络（LSTM）来捕捉时间序列中的依赖关系，同时引入注意力机制以增强模型对关键特征的关注度。通过在公开数据集及实际企业内网环境中进行实验验证，结果表明所提模型能够有效检测出多种类型的网络攻击行为，如DDoS攻击、恶意软件传播等，其准确率达到95%以上，且误报率低于5%，相较于传统方法具有显著优势。
关键词：深度学习 网络安全威胁预测 长短期记忆网络
Abstract
With the rapid development of information technology, network security threats are increasingly complex and changeable, and the traditional security protection means based on rules and features matching are gradually difficult to deal with new attacks. To this end, this study aims to construct a deep learning-based cybersecurity threat prediction model to achieve efficient identification and early warning of potential security risks. The study using the depth of neural network powerful nonlinear mapping ability, combined with the characteristics of the field of network security, selected, including traffic data, log information, heterogeneous data as input features, and the long and short-term memory network (LSTM) to capture the dependence in the time series, introduce attention mechanism at the same time to enhance the model of key features. Through the experimental verification in the public data set and the actual enterprise Intranet environment, the results show that the proposed model can effectively detect various types of network attacks, such as DDoS attack, malicious software propagation, etc., the accuracy is more than 95%, and the false alarm rate is less than 5%, which has significant advantages compared with traditional methods.
Keyword:Deep Learning Cybersecurity Threat Prediction Long Short-Term Memory Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 2
2深度学习算法选择与优化 2
2.1常见深度学习算法分析 2
2.2算法性能评估指标 3
2.3针对网络安全的算法优化 3
3网络安全威胁数据处理 4
3.1数据来源与采集方式 4
3.2数据预处理技术 4
3.3特征提取与选择 5
4模型设计与实现 6
4.1模型架构设计 6
4.2关键技术实现 7
4.3模型训练与调优 7
结论 8
参考文献 10
致谢 11
