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，传统攻击检测算法在应对新型网络攻击时面临诸多挑战，为提高网络安全攻击检测的有效性与准确性，本研究提出一种新颖的网络安全攻击检测算法。该算法融合深度学习与传统机器学习的优势，基于对海量网络流量数据的深入分析，构建包含特征提取、特征选择和分类识别三个核心模块的检测框架。特征提取采用改进的卷积神经网络自动挖掘网络流量中的潜在特征，特征选择引入粒子群优化算法以筛选最具区分度的特征子集，分类识别利用支持向量机实现高效准确的攻击判定。通过在公共数据集NSL - KDD上进行实验验证，结果表明该算法相较于传统检测方法，在检测率方面提升了约15%，误报率降低了约10%，能够有效检测出多种类型的网络攻击，包括DoS、Probe、R2L和U2R等。此算法创新地将深度学习与传统机器学习相结合用于网络安全攻击检测，不仅提高了检测性能，还为后续研究提供了新的思路与方向，对保障网络安全具有重要意义。
关键词：网络安全攻击检测；深度学习与机器学习融合；特征提取与选择
Abstract
With the rapid development of information technology, cybersecurity threats have become increasingly complex and diverse. Traditional attack detection algorithms face numerous challenges in addressing emerging cyber attacks. To enhance the effectiveness and accuracy of cybersecurity attack detection, this study proposes a novel cybersecurity attack detection algorithm that integrates the advantages of deep learning and traditional machine learning. By conducting an in-depth analysis of massive network traffic data, a detection fr amework comprising three core modules—feature extraction, feature selection, and classification identification—is constructed. Feature extraction employs an improved convolutional neural network to automatically uncover latent features within network traffic, while feature selection introduces particle swarm optimization to select the most discriminative feature subsets. Classification identification utilizes support vector machines for efficient and accurate attack determination. Experimental validation on the public NSL-KDD dataset demonstrates that this algorithm improves detection rates by approximately 15% and reduces false positive rates by about 10% compared to traditional detection methods. It effectively detects various types of network attacks, including DoS, Probe, R2L, and U2R. This algorithm innovatively combines deep learning with traditional machine learning for cybersecurity attack detection, not only enhancing detection performance but also providing new insights and directions for future research, which is of significant importance for ensuring cybersecurity.
Keywords：Cybersecurity Attack Detection; Integration Of Deep Learning And Machine Learning; Feature Extraction And Selection
目 录
摘要 I
Abstract II
一、绪论 1
(一) 网络安全攻击检测的研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法概述 2
二、新颖算法的设计原理 2
(一) 算法设计的基本思路 2
(二) 关键技术分析 3
(三) 算法模型构建 4
三、算法性能评估体系 5
(一) 评估指标的选取 5
(二) 测试环境搭建 5
(三) 实验结果分析 6
四、算法应用与验证 7
(一) 实际应用场景选择 7
(二) 检测效果对比分析 8
(三) 算法优化建议 9
结 论 11
参考文献 12
随着信息技术的迅猛发展，网络安全威胁日益复杂多变，传统攻击检测算法在应对新型网络攻击时面临诸多挑战，为提高网络安全攻击检测的有效性与准确性，本研究提出一种新颖的网络安全攻击检测算法。该算法融合深度学习与传统机器学习的优势，基于对海量网络流量数据的深入分析，构建包含特征提取、特征选择和分类识别三个核心模块的检测框架。特征提取采用改进的卷积神经网络自动挖掘网络流量中的潜在特征，特征选择引入粒子群优化算法以筛选最具区分度的特征子集，分类识别利用支持向量机实现高效准确的攻击判定。通过在公共数据集NSL - KDD上进行实验验证，结果表明该算法相较于传统检测方法，在检测率方面提升了约15%，误报率降低了约10%，能够有效检测出多种类型的网络攻击，包括DoS、Probe、R2L和U2R等。此算法创新地将深度学习与传统机器学习相结合用于网络安全攻击检测，不仅提高了检测性能，还为后续研究提供了新的思路与方向，对保障网络安全具有重要意义。
关键词：网络安全攻击检测；深度学习与机器学习融合；特征提取与选择
Abstract
With the rapid development of information technology, cybersecurity threats have become increasingly complex and diverse. Traditional attack detection algorithms face numerous challenges in addressing emerging cyber attacks. To enhance the effectiveness and accuracy of cybersecurity attack detection, this study proposes a novel cybersecurity attack detection algorithm that integrates the advantages of deep learning and traditional machine learning. By conducting an in-depth analysis of massive network traffic data, a detection fr amework comprising three core modules—feature extraction, feature selection, and classification identification—is constructed. Feature extraction employs an improved convolutional neural network to automatically uncover latent features within network traffic, while feature selection introduces particle swarm optimization to select the most discriminative feature subsets. Classification identification utilizes support vector machines for efficient and accurate attack determination. Experimental validation on the public NSL-KDD dataset demonstrates that this algorithm improves detection rates by approximately 15% and reduces false positive rates by about 10% compared to traditional detection methods. It effectively detects various types of network attacks, including DoS, Probe, R2L, and U2R. This algorithm innovatively combines deep learning with traditional machine learning for cybersecurity attack detection, not only enhancing detection performance but also providing new insights and directions for future research, which is of significant importance for ensuring cybersecurity.
Keywords：Cybersecurity Attack Detection; Integration Of Deep Learning And Machine Learning; Feature Extraction And Selection
目 录
摘要 I
Abstract II
一、绪论 1
(一) 网络安全攻击检测的研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法概述 2
二、新颖算法的设计原理 2
(一) 算法设计的基本思路 2
(二) 关键技术分析 3
(三) 算法模型构建 4
三、算法性能评估体系 5
(一) 评估指标的选取 5
(二) 测试环境搭建 5
(三) 实验结果分析 6
四、算法应用与验证 7
(一) 实际应用场景选择 7
(二) 检测效果对比分析 8
(三) 算法优化建议 9
结 论 11
参考文献 12
