软件导刊2023，Vol.22Issue(12)：185-191,7.DOI:10.11907/rjdk.222219
大数据背景下基于PCA-DELM的入侵检测研究
Research on Intrusion Detection Based on PCA-DELM in the Background of Big Data
王振东 1王思如 1王俊岭 1李大海1
作者信息
- 1. 江西理工大学 信息工程学院,江西 赣州 341000
- 折叠
摘要
Abstract
The types and forms of malicious attacks are constantly changing,and the number of attacks is gradually increasing.Traditional neural network model architecture plays an important role in improving model accuracy,reducing model computation and improving reasoning speed,etc.However,traditional model architecture requires a lot of computing resources in search,and its generalization ability is not high.In this regard,it is necessary to propose solutions for network attacks in the context of big data.Based on the application of deep learning in network security,combined with principal component analysis(PCA)and deep Extreme Learning Machine(DELM)in the field of intrusion detection,a lightweight neural network PCA-DELM is designed to reduce computing resources and improve generalization ability while retain-ing the advantages of traditional neural network model architecture.The simulation results show that compared with other algorithms,the opti-mized lightweight neural network model PCA-DELM can significantly improve the ability of intrusion detection and speed up the detection rate on different data sets.关键词
入侵检测/网络安全/深度极限学习机/主成分分析/深度学习Key words
intrusion detection/network security/extreme learning machine/principal component analysis/deep learning分类
信息技术与安全科学引用本文复制引用
王振东,王思如,王俊岭,李大海..大数据背景下基于PCA-DELM的入侵检测研究[J].软件导刊,2023,22(12):185-191,7.
