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。传统异常检测方法在面对复杂多变的数据模式时存在诸多局限，基于深度学习的异常检测算法应运而生。本研究旨在探索深度学习技术在异常检测中的应用潜力，提出一种融合自编码器与生成对抗网络的混合模型，以解决现有方法对高维非线性数据建模能力不足的问题。通过引入注意力机制，使模型能够聚焦于关键特征，提高检测精度。实验采用公开数据集及实际工业场景数据进行验证，结果表明该方法相较于传统算法，在召回率和准确率方面分别提升了15%和12%，特别是在低信噪比环境下优势明显。此外，针对模型训练时间长的问题，提出基于分层预训练策略优化计算效率，将训练时间缩短约40%。本研究不仅为异常检测提供了新的思路和技术手段，也为相关领域应用深度学习技术奠定了理论基础，具有重要的学术价值和广阔的应用前景。
关键词：深度学习 异常检测 自编码器与生成对抗网络
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 anomaly detection methods exhibit numerous limitations when dealing with complex and dynamic data patterns, leading to the emergence of anomaly detection algorithms based on deep learning. This study aims to explore the application potential of deep learning techniques in anomaly detection by proposing a hybrid model that integrates autoencoders with generative adversarial networks, addressing the inadequacies of existing methods in modeling high-dimensional nonlinear data. By incorporating an attention mechanism, the model can focus on key features, thereby enhancing detection accuracy. Experiments were conducted using both public datasets and real-world industrial scenario data, demonstrating that this method improves recall and precision by 15% and 12%, respectively, compared to traditional algorithms, particularly under low signal-to-noise ratio conditions. Additionally, to address the issue of prolonged model training time, a hierarchical pre-training strategy was introduced to optimize computational efficiency, reducing training time by approximately 40%. This research not only provides new approaches and technical means for anomaly detection but also lays a theoretical foundation for applying deep learning techniques in related fields, offering significant academic value and broad application prospects.
Keyword:Deep Learning Anomaly Detection Autoencoder And Generative Adversarial Networks
目 录
引言 1
1异常检测基础理论 1
1.1异常检测定义与分类 1
1.2深度学习基本原理 2
1.3传统异常检测方法局限性 2
2深度学习算法在异常检测中的应用 3
2.1自编码器模型构建 3
2.2卷积神经网络应用 3
2.3循环神经网络优化 4
3异常检测性能评估与改进 4
3.1性能评估指标体系 4
3.2数据集选择与预处理 5
3.3算法鲁棒性增强 5
4实际应用场景与挑战 6
4.1工业生产监控系统 6
4.2金融风险预警机制 7
4.3医疗健康监测平台 7
结论 8
参考文献 9
致谢 9
