Network Intrusion Detection through Online Transformation of Eigenvector Reflecting Concept Drift

Abstract

Recently, large amount data streams are increasing. It is difficult to continuously store data and perform the principal component analysis in periodical offline (batch) mode. To solve this problem, there is a need to reflect the concept drift through online transformation to obtain the eigenvector, which is the goal of the principal component analysis. In this study, we compared the performance of online mode using the online eigenvector transformation in the network intrusion detection with offline mode. Both of them are applied through a multinomial logistic regression(MLR). The results showed that both the online and offline mode demonstrated excellent performance in accuracy, but the multinomial logistic regression applied with the online eigenvector transformation showed better performance in recall and as a result, the F1-Measure was also better.