摘 要
随着信息技术的迅猛发展，数据量呈爆炸式增长，异常检测在网络安全、工业监控等领域的重要性日益凸显。传统基于统计和规则的方法难以应对复杂多变的数据模式，基于神经网络的异常检测算法应运而生。本研究旨在构建一种高效准确的异常检测模型，以适应现代数据环境下的需求。为此，提出了一种融合自编码器与长短期记忆网络的混合架构，该架构能够自动学习数据特征并捕捉时间序列中的长期依赖关系。通过引入注意力机制，使模型可以聚焦于关键特征，提高检测精度。实验采用公开数据集及实际应用场景中的数据进行验证，结果表明所提方法相较于传统方法及单一神经网络模型，在检测率、误报率等指标上均有显著提升。特别是在处理高维、非线性数据时优势明显。此外，针对模型训练过程中的过拟合问题，采用正则化技术加以改进，确保模型具有良好的泛化能力。本研究不仅为异常检测提供了新的思路和技术手段，还为神经网络在相关领域的应用拓展了方向，对推动智能检测技术的发展具有重要意义。
关键词：异常检测；神经网络；自编码器；长短期记忆网络；注意力机制
Abstract
With the rapid development of information technology and the explosive growth of data volumes, anomaly detection has become increasingly critical in areas such as cybersecurity and industrial monitoring. Traditional methods based on statistics and rules struggle to cope with the complex and dynamic patterns of modern data, leading to the emergence of anomaly detection algorithms based on neural networks. This study aims to develop an efficient and accurate anomaly detection model that can meet the demands of contemporary data environments. To this end, a hybrid architecture integrating autoencoders and long short-term memory (LSTM) networks is proposed. This architecture can automatically learn data features and capture long-term dependencies in time series data. By incorporating an attention mechanism, the model is able to focus on key features, thereby enhancing detection accuracy. Experiments were conducted using both public datasets and real-world application data, demonstrating that the proposed method significantly outperforms traditional methods and single neural network models in terms of detection rate and false positive rate, particularly when dealing with high-dimensional and nonlinear data. Additionally, to address overfitting during the training process, regularization techniques were employed to ensure the model's generalization capability. This research not only provides new approaches and technical means for anomaly detection but also expands the application prospects of neural networks in related fields, contributing significantly to the advancement of intelligent detection technologies.
Keywords：Anomaly Detection；Neural Network；Autoencoder；Long Short-Term Memory Network；Attention Mechanism
目 录
摘 要 I
Abstract II
引 言 1
第一章 神经网络基础与异常检测概述 2
1.1 神经网络基本原理 2
1.2 异常检测定义与挑战 2
1.3 常见异常检测方法比较 3
第二章 基于神经网络的异常检测模型构建 5
2.1 模型架构设计原则 5
2.2 特征提取与表示学习 5
2.3 损失函数选择与优化 6
第三章 异常检测算法性能评估 8
3.1 评价指标体系建立 8
3.2 实验数据集选取 8
3.3 不同场景下的性能分析 9
第四章 应用案例与未来发展方向 11
4.1 工业领域应用实例 11
4.2 医疗健康领域应用 11
4.3 算法改进与展望 12
结 论 14
参考文献 15
致 谢 16
