Prediction of Computer Network Security Situation Based on Association Rules Mining

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.