Prediction of Computer Network Security Situation Based on Association Rules Mining
Rixin Xue, Peng Tang, Shudong Fang
Abstract
Traditional NSSA (network security situational awareness) systems have significant equipment limitations, poor data fusion capabilities, and a low level of analysis and evaluation, making them difficult to adapt to large‐scale and complex network environments. This paper proposes the study of computer NSS (network security situation) prediction technology based on AR (association rules) mining to solve this problem. The support‐confidence framework is improved by introducing an interest evaluation standard, and the value of AR is re‐evaluated, based on a discussion of traditional concepts and algorithms related to AR mining. The MFP‐interest algorithm proposed in this paper is a combination of alarm AR template and interest degree. The MFP‐interest algorithm was put to the test. We discovered that the MFP‐interest algorithm can effectively predict NSS and indicate its development trend when run in a real‐world network environment. Most time points have a relative error range of less than 0.035.