Inter-domain Link Inference with Confidence Using Naïve Bayes Classifier

Abstract

Inter-domain link inference is not only important for network security and fault diagnosis, but also helps to conduct research on inter-domain congestion detection and network resilience assessment. Current researches on this issue lack confidence analysis of the inferred results. In this paper, the IP link types (i.e., intra-domain link and inter-domain link) are considered as the latent variable in probability model, while the parameters are probabilities of different link types with particular features. The expectation maximization algorithm is applied to estimate parameters of the model. In each iteration of EM algorithm, Naïve Bayes is used for classification. The final result is determined according to the probability, and the probability is the confidence of the result. The experimental results show that our method can achieve better precision and recall on the validation set than two existing general methods.