基于DeepFM的恶意流量识别技术分析
Analysis of malicious traffic recognition technology based on DeepFM
冯群浩1
作者信息
- 1. 广东茂名健康职业学院,广东 525400
- 折叠
摘要
阐述一种基于DeepFM的恶意流量识别模型,该模型结合因子分解机(FM)和深度神经网络(DNN)的优势,通过设计多层深度神经网络结构,实现低阶和高阶特征交互,以提升模型预测性能.
Abstract
This paper describes a malicious traffic recognition model based on DeepFM,which combines the advantages of Factorization Machines(FM)and Deep Neural Networks(DNN).By designing a multi-layer deep neural network structure,it achieves low-and high-order feature interaction to improve the model's predictive performance.
关键词
恶意流量/网络安全/DeepFM/深度学习/特征交互Key words
malicious traffic/network security/DeepFM/deep learning/feature interaction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据