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

恶意攻击类型及形式不断变化,攻击量逐渐增加,传统神经网络模型架构在提高模型精度、减少模型计算量、提高推理速度等方面起着重要作用,然而,传统模型架构搜索时需消耗大量计算资源,且泛化能力不高。对此,需提出针对大数据背景下网络攻击的解决方案。基于深度学习在网络安全方面的应用,在入侵检测领域结合主成分分析方法(PCA)并使用深度极限学习机(DELM)进行研究,设计一种轻量级神经网络PCA-DELM,在保留传统神经网络模型架构优点的同时,减小计算资源,提升泛化能力。仿真结果表明,相较于其他算法,优化后的轻量级神经网络模型PCA-DELM在不同的数据集上能显著提高入侵检测能力,加快检测速率。

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.