Bayesian Statistical Inference in Machine Learning Anomaly Detection

Intrusion Detection is an important component of the security of the Network, through the critical information of the network and host system, it can determine the user´s invasion of illegal and legal acts of the user misuse of resources, and make an adequate response. According to the problem, which machine learning anomaly detection effect is not ideal when the user behavior changes and a separate anomaly detection. Based on the Bayesian inference anomaly detection, applying the Bayesian inference of statistical methods to machine learning anomaly detection, this paper established a decision tree corresponding to the method. This method overcomes the satisfactory of the anomaly detection individual test, and improves the machine learning in the predictive ability of anomaly detection and anomaly detection efficiency.