Network real-time risk assessment based on hidden Markov models
Yu Ma · Jisuanji gongcheng yu sheji · 2009
In the field of network risk assessment based on hidden Markov models,the state transition matrix are usually derived from expert experience.It leads to much subjectives to the result of the assessment which can not objectively reflect the real risk of the network. Therefore,a conception of attack difficulty coefficient is introduced.Through analyzing and statistical learning the dataset,the state transition matrix is derived.Taxonomy of those threats are done in the dataset,then weights all of them according to their influence. The experiment indicates that the result of the assessment using the method proposed is much more objective while providing better support for network risk management.