基于双层异质集成学习器的入侵检测方法

Intrusion Detection Method based on Double-Layer Heterogeneous Ensemble Learner

入侵检测是网络安全领域中具有挑战性和重要性的任务。现有研究以增加时间消耗和误报率为代价,重点关注如何提高检测率,在实际应用中代价较大。为此,本文提出了一种使用双层异质学习器集成学习策略的入侵检测IDHEL模型。该模型使用概率核主成分分析方法降低数据维度,采用多个异质分类器通过分层十折交叉验证策略进行异常检测,并根据所提出的分类器评估算法筛选出在相关数据上表现最佳的三种分类器,基于概率加权投票的多分类器集成算法进行入侵检测。实验结果表明IDHEL模型在准确率、错误率和时间消耗方面均优于现有主流入侵检测模型。

Intrusion detection is a challenging and important task.Nowadays,researchers proposed many intrusion detection models and technologies.However,existing research focused on how to increase detection rate at the cost of increasing time consumption and false positive rate,which is costly in practical application.In this paper,we propose a novel intrusion detection model using double-layer heterogeneous ensemble learner strategy(IDHEL).This model first uses probabilistic kernel principal component analysis to efficiently reduce the data dimension,in order to reduce the computational overhead.Then,multiple heterogeneous classifiers are adopted for anomaly detection by a layered ten-fold cross validation strategy.Finally,IDHEL chooses the best three classifiers based on probability-weighted voting for intrusion detection.We compare the IDHEL model with existing algorithms,and the experimental results have shown that the IDHEL model is superior to other models in terms of accuracy,False Positive Rate(FPR)and time consumption.