基于深度学习的网络诈骗检测技术研究

摘要：本篇网络诈骗检测技术研究旨在探讨深度学习技术在网络安全领域中的应用，针对当前网络环境中日益增多的网络诈骗行为，提出基于深度学习的网络诈骗检测方法。本文在深度学习技术介绍的基础上，详细阐述了卷积神经网络（CNN）、循环神经网络（RNN）和生成对抗网络（GAN）在网络诈骗检测中的应用，并通过实验设计验证了方法的准确性和稳定性，同时提出了一种新的基于深度学习的网络诈骗检测模型，并对实验结果和创新模型进行了详细的分析和总结。本文的研究成果有助于提高网络安全环境下的统和管理，促进网络诈骗检测技术的发展，并对相关企业和机构提供更加有效的保护措施和安全服务。

关键词：网络诈骗检测，深度学习，卷积神经网络，循环神经网络，生成对抗网络

Abstract:This research on network fraud detection technology aims to explore the application of deep learning technology in the field of network security, and put forward a network fraud detection method based on deep learning in view of the increasing network fraud behavior in the current network environment. Based on the introduction of deep learning technology, this paper elaborates the application of convolutional neural network (CNN), recurrent neural network (RNN) and generative adversarial network (GAN) in network fraud detection, verifies the accuracy and stability of the methods through experimental design, and proposes a new network fraud detection model based on deep learning. The experimental results and innovative models are analyzed and summarized in detail. The research results of this paper are helpful to improve the management of network security environment, promote the development of network fraud detection technology, and provide more effective protection measures and security services for related enterprises and institutions.

Key words：Network fraud detection, deep learning, convolutional neural networks, recurrent neural networks, generative adversarial networks

题目：基于深度学习的网络诈骗检测技术研究 1

摘要： 1

1 绪论 2

1.1研究背景和意义 2

1.2国内外研究现状和进展 2

1.3研究方法和内容 2

2.深度学习在网络诈骗检测中的应用 3

2.1深度学习技术简介 3

2.2卷积神经网络（CNN）在网络诈骗检测中的应用 3

2.3循环神经网络（RNN）在网络诈骗检测中的应用 4

2.4生成对抗网络（GAN）在网络诈骗检测中的应用 4