Research on malicious traffic detection and defense technology based on deep learning
基于深度学习的恶意流量检测与防御技术研究

摘要

随着网络攻击手段的不断演进，传统的恶意流量检测方法已难以满足复杂环境下的安全需求。基于深度学习的恶意流量检测技术，通过自动提取数据特征和多层次模式识别，显著提升了检测的准确性与实时性。本文围绕深度学习模型在恶意流量识别中的应用，探讨了多种主流网络攻击场景下的检测策略及其防御机制。研究表明，结合深度神经网络的智能分析方法不仅能有效识别复杂攻击，还能动态调整防御策略，实现对恶意流量的高效防护，推动网络安全防御技术向智能化发展迈进。

Abstract

As cyber attack methods continue to evolve, traditional malicious traffic detection techniques have become inadequate for the security requirements in complex environments. Deep learning-based malicious traffic detection technology significantly enhances accuracy and real-time performance through automatic data feature extraction and multi-level pattern recognition. This paper focuses on the application of deep learning models in malicious traffic identification, exploring detection strategies and defense mechanisms under various mainstream network attack scenarios. The study shows that intelligent analysis methods combining deep neural networks can not only effectively identify complex attacks but also dynamically adjust defense strategies, achieving efficient protection against malicious traffic and advancing cybersecurity defense technology towards smarter development.