摘 要
随着无线局域网(WLAN)的广泛应用，其面临的安全威胁日益严峻，传统的安全防护机制难以应对复杂多变的攻击形式。为此，本研究旨在利用机器学习技术提升WLAN安全威胁识别能力。通过构建基于多种机器学习算法的威胁识别模型，包括支持向量机、随机森林和深度神经网络等，对不同类型的安全威胁进行特征提取与分类识别。研究收集了大量实际网络环境中的流量数据作为训练样本，涵盖常见的拒绝服务攻击、中间人攻击、非法接入等多种典型威胁场景，并对数据进行了预处理以确保样本质量。实验结果表明，所提出的基于机器学习的识别方法能够有效区分正常流量与恶意流量，在准确率、召回率等关键性能指标上均优于传统基于规则的方法，其中深度神经网络模型在复杂场景下的表现尤为突出，准确率达到95%以上。
关键词：无线局域网安全 机器学习 威胁识别
Abstract
With the wide application of wireless local area network (WLAN), its security threats are increasingly severe, and the traditional security protection mechanism is difficult to deal with the complex and changeable forms of attack. To this end, this study aims to use machine learning technology to improve the WLAN security threat identification ability. By constructing a threat recognition model based on multiple machine learning algorithms, including support vector machine, random forest and deep neural network, the feature extraction and classification of different types of security threats are identified. The study collected a large number of traffic data in real network environment as training samples, covering many typical threat scenarios such as common denial-of-service attack, middleman attack and illegal access, and the data was preprocessed to ensure sample quality. The experimental results show that the proposed machine learning-based identification method can effectively distinguish normal traffic from malicious traffic, and is better than the traditional rule-based methods in key performance indicators such as accuracy and recall rate. Among them, the deep neural network model is particularly prominent in complex scenarios, with an accuracy of more than 95%.
Keyword:Wireless Local Area Network Security Machine Learning Threat Identification
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 1
2无线局域网安全威胁分析 2
2.1常见安全威胁类型 2
2.2威胁特征提取方法 2
2.3威胁数据集构建 3
3机器学习算法选择与优化 4
3.1算法性能比较 4
3.2特征选择与降维 5
3.3模型参数调优 5
4安全威胁识别系统设计与实现 6
4.1系统架构设计 6
4.2关键技术实现 6
4.3实验结果与分析 7
结论 8
参考文献 9
致谢 10
随着无线局域网(WLAN)的广泛应用，其面临的安全威胁日益严峻，传统的安全防护机制难以应对复杂多变的攻击形式。为此，本研究旨在利用机器学习技术提升WLAN安全威胁识别能力。通过构建基于多种机器学习算法的威胁识别模型，包括支持向量机、随机森林和深度神经网络等，对不同类型的安全威胁进行特征提取与分类识别。研究收集了大量实际网络环境中的流量数据作为训练样本，涵盖常见的拒绝服务攻击、中间人攻击、非法接入等多种典型威胁场景，并对数据进行了预处理以确保样本质量。实验结果表明，所提出的基于机器学习的识别方法能够有效区分正常流量与恶意流量，在准确率、召回率等关键性能指标上均优于传统基于规则的方法，其中深度神经网络模型在复杂场景下的表现尤为突出，准确率达到95%以上。
关键词：无线局域网安全 机器学习 威胁识别
Abstract
With the wide application of wireless local area network (WLAN), its security threats are increasingly severe, and the traditional security protection mechanism is difficult to deal with the complex and changeable forms of attack. To this end, this study aims to use machine learning technology to improve the WLAN security threat identification ability. By constructing a threat recognition model based on multiple machine learning algorithms, including support vector machine, random forest and deep neural network, the feature extraction and classification of different types of security threats are identified. The study collected a large number of traffic data in real network environment as training samples, covering many typical threat scenarios such as common denial-of-service attack, middleman attack and illegal access, and the data was preprocessed to ensure sample quality. The experimental results show that the proposed machine learning-based identification method can effectively distinguish normal traffic from malicious traffic, and is better than the traditional rule-based methods in key performance indicators such as accuracy and recall rate. Among them, the deep neural network model is particularly prominent in complex scenarios, with an accuracy of more than 95%.
Keyword:Wireless Local Area Network Security Machine Learning Threat Identification
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 1
2无线局域网安全威胁分析 2
2.1常见安全威胁类型 2
2.2威胁特征提取方法 2
2.3威胁数据集构建 3
3机器学习算法选择与优化 4
3.1算法性能比较 4
3.2特征选择与降维 5
3.3模型参数调优 5
4安全威胁识别系统设计与实现 6
4.1系统架构设计 6
4.2关键技术实现 6
4.3实验结果与分析 7
结论 8
参考文献 9
致谢 10
