基于联邦学习和深度学习的入侵检测方法研究 -计算机科学与技术专业

摘要

随着网络技术的飞速发展，网络安全问题日益凸显，其中入侵检测作为网络安全的重要组成部分，对于保障网络系统的安全性具有重要意义。本文提出了一种基于联邦学习和深度学习的入侵检测方法，旨在提高入侵检测的准确性和鲁棒性。本文介绍了深度学习和联邦学习的基本理论，并分析了它们在入侵检测中的应用前景。设计了一种基于联邦学习和深度学习的入侵检测系统架构，包括数据处理与特征提取、模型训练与优化以及性能评估等环节。通过实验验证，该方法在多个数据集上均取得了较高的检测准确率和良好的泛化性能。针对数据隐私与安全性问题、通信效率与资源消耗问题以及模型泛化与鲁棒性问题进行了深入分析，并提出了相应的解决对策。本文提出的基于联邦学习和深度学习的入侵检测方法为网络安全领域提供了一种新的解决方案，具有重要的理论意义和应用价值。

关键词：联邦学习深度学习入侵检测

Abstract

With the rapid development of network technology, the problem of network security becomes more and more prominent. As an important part of network security, intrusion detection plays an important role in ensuring the security of network system. This paper proposes an intrusion detection method based on federation learning and deep learning to improve the accuracy and robustness of intrusion detection. This paper introduces the basic theories of deep learning and federation learning, and analyzes their application prospects in intrusion detection. An intrusion detection system architecture based on federation learning and deep learning is designed, including data processing and feature extraction, model training and optimization, and performance evaluation. Experimental results show that the proposed method achieves high detection accuracy and good generalization performance on multiple data sets. The problems of data privacy and security, communication efficiency and resource consumption, model generalization and robustness are deeply analyzed, and corresponding solutions are proposed. The intrusion detection method based on federation learning and deep learning proposed in this paper provides a new solution for the field of network security, which has important theoretical significance and application value.

Keyword:Federal learning Deep learning Intrusion detection

1绪论 1

1.1研究背景及意义 1

1.2国内外研究现状 1

1.3研究内容及目的 1

2相关理论概述 1

2.1深度学习理论 1

2.2联邦学习理论 2

2.3入侵检测理论 2

3基于联邦学习和深度学习的入侵检测分析 2

3.1系统架构设计 2

3.2数据处理与特征提取 3