摘 要
随着网络攻击手段的日益复杂化与多样化，传统基于规则和签名的入侵检测系统在实时性、准确性和应对新型攻击方面面临严峻挑战。为提升网络安全防护能力，本文研究并设计了一种基于人工智能的网络入侵检测系统，旨在通过深度学习与机器学习技术有效识别异常行为与攻击模式。本研究选取多种网络流量数据集进行预处理与特征提取，采用改进的深度神经网络模型，并结合集成学习策略优化检测性能。实验过程中引入交叉验证机制，以确保模型的泛化能力与稳定性。结果表明，所提出的检测模型在多个评估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高的准确率与较低的误报率。此外，本文提出了一种动态特征加权机制，能够根据网络环境变化自适应调整特征重要性，从而提升检测系统的灵活性与鲁棒性。研究表明，人工智能技术在网络入侵检测领域具有显著优势，能够有效增强对复杂攻击的识别能力。本工作的主要贡献在于构建了一个高效、智能且具备自适应能力的入侵检测框架，为未来网络安全防御体系的发展提供了新的思路和技术支持。
关键词：人工智能；入侵检测系统；深度神经网络；集成学习；动态特征加权
Abstract
With the increasing complexity and diversity of network attack methods, traditional rule-based and signature-based intrusion detection systems face significant challenges in terms of real-time performance, accuracy, and the ability to respond to emerging attacks. To enhance cybersecurity defense capabilities, this study investigates and designs an artificial intelligence-based network intrusion detection system, aiming to effectively identify abnormal behaviors and attack patterns through deep learning and machine learning techniques. Multiple network traffic datasets are selected for preprocessing and feature extraction, and an improved deep neural network model combined with ensemble learning strategies is employed to optimize detection performance. A cross-validation mechanism is introduced during the experiments to ensure the model's generalization ability and stability. Results demonstrate that the proposed detection model outperforms existing mainstream approaches across multiple evaluation metrics, particularly exhibiting higher accuracy and lower false positive rates in detecting unknown attack types. Furthermore, this study proposes a dynamic feature weighting mechanism capable of adaptively adjusting feature importance according to changes in the network environment, thereby enhancing the flexibility and robustness of the detection system. Research findings indicate that artificial intelligence technologies offer significant advantages in the field of network intrusion detection and can effectively strengthen the capability to recognize complex attacks. The primary contribution of this work lies in constructing an efficient, intelligent, and adaptive intrusion detection fr amework, providing new insights and technical support for the development of future cybersecurity defense systems.
Keywords: Artificial Intelligence; Intrusion Detection System; Deep Neural Network; Ensemble Learning; Dynamic Feature Weighting
目 录
1绪论 1
1.1研究背景和意义 1
1.2研究现状 1
1.3本文研究方法 1
2基于人工智能的网络入侵检测系统架构设计 2
2.1系统总体架构与功能模块划分 2
2.2数据采集与预处理机制构建 2
2.3特征提取与表示学习方法分析 3
2.4模型部署与实时检测流程设计 3
3基于深度学习的入侵行为识别模型研究 4
3.1卷积神经网络在流量特征提取中的应用 4
3.2循环神经网络对时序攻击模式的建模能力 4
3.3自编码器在异常检测中的优化策略 5
3.4多模型融合提升检测准确率的方法探讨 5
4基于人工智能的入侵检测系统性能评估与优化 6
4.1系统评估指标体系的构建 6
4.2在KDD Cup 99与NSL-KDD数据集上的实验分析 6
4.3面向不平衡数据的采样与训练策略 7
4.4检测延迟与资源消耗的优化路径 7
结论 8
参考文献 10
致 谢 11
