基于残差卷积神经网络的网络攻击检测技术研究
摘 要：随着网络攻击手段的日益复杂化和隐蔽化，传统基于特征匹配的检测方法在面对新型攻击时表现出明显的局限性。为提升网络攻击检测的准确率与泛化能力，本文提出了一种基于残差卷积神经网络（Residual CNN）的深度学习检测模型。该模型通过引入残差结构缓解深层网络中的梯度消失问题，从而有效提升模型训练效率与特征提取能力。研究设计了一种面向流量数据的多层卷积架构，结合批量归一化与跳跃连接机制，实现对原始流量数据中局部特征与高阶语义特征的融合学习。实验基于CIC-IDS2017公开数据集，采用标准化预处理流程，并通过十折交叉验证评估模型性能。结果表明，所提方法在检测准确率、F1分数及误报率等关键指标上均优于现有主流方法，尤其在检测未知攻击类型时展现出更强的鲁棒性。本研究的主要创新在于将残差学习机制引入网络攻击检测领域，并验证其在处理高维非结构化流量数据中的有效性，为构建高效、自适应的安全防护体系提供了新的技术路径。
关键词：残差卷积神经网络；网络攻击检测；深度学习模型；流量数据特征融合；未知攻击鲁棒性
Research on Network Attack Detection Techniques Based on Residual Convolutional Neural Networks
Abstract: With the increasing sophistication and stealthiness of network attack techniques, traditional detection methods based on feature matching have demonstrated significant limitations when confronting emerging attack patterns. To enhance the accuracy and generalization capability of network attack detection, this paper proposes a deep learning detection model based on a residual convolutional neural network (Residual CNN). By incorporating residual structures, the proposed model mitigates the problem of gradient vanishing in deep networks, thereby effectively improving training efficiency and feature extraction capabilities. The study designs a multi-layer convolutional architecture tailored for traffic data, which integrates batch normalization and skip connection mechanisms to enable the fusion learning of local features and high-order semantic features from raw traffic data. The experiments are conducted on the publicly available CIC-IDS2017 dataset, employing a standardized preprocessing pipeline and evaluating model performance through ten-fold cross-validation. Results indicate that the proposed method outperforms existing state-of-the-art approaches in key metrics such as detection accuracy, F1 score, and false positive rate, demonstrating particularly stronger robustness in detecting unknown attack types. The primary innovation of this research lies in introducing the residual learning mechanism into the domain of network attack detection and validating its effectiveness in processing high-dimensional, unstructured traffic data, thus offering a novel technical pathway for building efficient and adaptive security defense systems.
Keywords: Residual Convolutional Neural Network; Network Attack Detection; Deep Learning Model; Traffic Data Feature Fusion; Unknown Attack Robustness
目 录
1绪论 1
1.1研究背景和意义 1
1.2国内外研究现状 1
1.3研究方法与内容 1
2残差卷积神经网络模型设计与优化 2
2.1残差模块在攻击特征提取中的作用分析 2
2.2基于ResNet改进的轻量化网络结构设计 2
2.3多尺度特征融合机制构建与实现 3
2.4模型训练策略与参数调优方法 3
3网络攻击数据预处理与特征工程 4
3.1网络攻击数据集的选择与标准化处理 4
3.2数据增强技术在不平衡样本中的应用 4
3.3特征编码与输入表示方式对比分析 5
3.4攻击类型分类体系的构建与映射 5
4攻击检测模型的实验与性能评估 6
4.1实验环境配置与评估指标设定 6
4.2不同攻击场景下的检测准确率分析 6
4.3与其他深度学习模型的性能对比 7
4.4模型鲁棒性与泛化能力测试 7
结论 8
参考文献 9
致 谢 10
