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Article summary:

1. A computer network security risk assessment model is proposed to strengthen the active defense of computer network security.

2. The model uses mrmr Ig feature selection model to classify the characteristics of security events and hidden Markov chain model and attack graph model to predict attack intention.

3. The experimental results show that the classification accuracy rate of the model is 99.79%, with a low false alarm rate and false alarm rate.

Article analysis:

The article provides an overview of a computer network security risk assessment model which is designed to improve the classification and prediction performance of computer network security events, as well as provide reference for timely taking targeted computer network security reinforcement and active defense measures. The article appears to be reliable in terms of its claims, as it provides evidence from experiments that demonstrate its effectiveness in accurately predicting attacks in real time with a high accuracy rate and low false alarm rates. However, there are some potential biases that should be noted when considering this article. For example, the article does not explore any counterarguments or alternative approaches to computer network security evaluation, nor does it present both sides equally or consider possible risks associated with using this approach. Additionally, there may be some promotional content in the article as it focuses solely on the benefits of this particular approach without exploring any potential drawbacks or limitations. In conclusion, while this article appears to be reliable overall, further research should be conducted into other approaches for evaluating computer network security before making any decisions about implementation.