Estimating computer network security scenarios with association rules

Abstract

Traditional NSSA (network security situational awareness) systems are problems familiarize to enormous for complicated network circumstances due to instrument limitations, inadequate and data fusion capabilities. This paper proposes the investigation of Association Rules (AR) based NSS (network security situation) prediction technology. Mining for regulations for associations to remedy this issue re- confidence support framework is enhanced by incorporating an interest. The assessment standard is revised, and the worth of AR is reassessed established on a discussion of associated standard ideas and algorithms AR mining. This research proposes an MFP-interest algorithm that combines an alert AR pattern with a degree of interest. The MFP-interest algorithm was evaluated. The researcher found that the MFP-interest algorithm is capable of accurately predicting NSS. The majority of time points have relative error ranges of less than 0.035.