摘 要
随着互联网技术的迅猛发展，分布式拒绝服务（DDoS）攻击成为威胁局域网安全的重要因素，其攻击手段日益复杂且隐蔽，传统检测与防御机制面临严峻挑战。本研究旨在深入分析局域网中DDoS攻击的特点，构建高效精准的检测与防御体系。通过融合机器学习算法与流量特征分析，提出一种基于深度神经网络的异常流量识别模型，该模型能够实时监测网络流量并准确区分正常流量与攻击流量，同时引入自适应阈值调整机制以应对不同规模的攻击场景。实验结果表明，所提出的检测方法在准确性、召回率等方面均优于现有主流方案，特别是在低强度攻击检测方面表现出色。
关键词：分布式拒绝服务攻击 深度神经网络 异常流量识别
Abstract
With the rapid development of Internet technology, distributed denial of service (DDoS) attacks have become an important factor threatening LAN security, its attack means are increasingly complex and hidden, and the traditional detection and defense mechanism is facing severe challenges. The purpose of this study is to deeply analyze the characteristics of DDoS attacks in LAN and build an efficient and accurate detection and defense system. By integrating machine learning algorithm and traffic feature analysis, an abnormal traffic identification model based on deep neural network is proposed. This model can monitor network traffic in real time and accurately distinguish normal traffic from attack traffic, and introduce adaptive threshold adjustment mechanism to deal with attack scenarios of different sizes. The experimental results show that the proposed detection method is better than the existing mainstream schemes in terms of accuracy and recall rate, especially in low-intensity attack detection.
Keyword:Distributed Denial Of Service Attack Deep Neural Network Anomaly Traffic Detection
目 录
1绪论 1
1.1局域网 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局域网DDoS攻击特征分析 2
2.2局域网环境下的攻击特点 2
2.3常见攻击类型及行为模式 3
3局域网DDoS攻击检测技术 3
3.1检测机制设计原则 3
3.2流量监测与异常识别 4
3.3智能化检测算法应用 5
4局域网DDoS防御策略构建 5
4.1防御体系架构设计 5
4.2关键防御技术实现 6
4.3防护效果评估方法 6
结论 7
参考文献 8
致谢 9
随着互联网技术的迅猛发展，分布式拒绝服务（DDoS）攻击成为威胁局域网安全的重要因素，其攻击手段日益复杂且隐蔽，传统检测与防御机制面临严峻挑战。本研究旨在深入分析局域网中DDoS攻击的特点，构建高效精准的检测与防御体系。通过融合机器学习算法与流量特征分析，提出一种基于深度神经网络的异常流量识别模型，该模型能够实时监测网络流量并准确区分正常流量与攻击流量，同时引入自适应阈值调整机制以应对不同规模的攻击场景。实验结果表明，所提出的检测方法在准确性、召回率等方面均优于现有主流方案，特别是在低强度攻击检测方面表现出色。
关键词：分布式拒绝服务攻击 深度神经网络 异常流量识别
Abstract
With the rapid development of Internet technology, distributed denial of service (DDoS) attacks have become an important factor threatening LAN security, its attack means are increasingly complex and hidden, and the traditional detection and defense mechanism is facing severe challenges. The purpose of this study is to deeply analyze the characteristics of DDoS attacks in LAN and build an efficient and accurate detection and defense system. By integrating machine learning algorithm and traffic feature analysis, an abnormal traffic identification model based on deep neural network is proposed. This model can monitor network traffic in real time and accurately distinguish normal traffic from attack traffic, and introduce adaptive threshold adjustment mechanism to deal with attack scenarios of different sizes. The experimental results show that the proposed detection method is better than the existing mainstream schemes in terms of accuracy and recall rate, especially in low-intensity attack detection.
Keyword:Distributed Denial Of Service Attack Deep Neural Network Anomaly Traffic Detection
目 录
1绪论 1
1.1局域网 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局域网DDoS攻击特征分析 2
2.2局域网环境下的攻击特点 2
2.3常见攻击类型及行为模式 3
3局域网DDoS攻击检测技术 3
3.1检测机制设计原则 3
3.2流量监测与异常识别 4
3.3智能化检测算法应用 5
4局域网DDoS防御策略构建 5
4.1防御体系架构设计 5
4.2关键防御技术实现 6
4.3防护效果评估方法 6
结论 7
参考文献 8
致谢 9
