An Efficient Intrusion Detection Model Based on Fast Inductive Learning

In recent years, intelligent intrusion detection techniques based on machine learning have been the research spots in the field of intrusion detection. Whereas, as network traffic and network scale increase continually, some current machine learning algorithms can´t meet the requirement of the network intrusion detection models for efficiency and accuracy, which restricts the application of machine learning into intrusion detection. In order to enhance the availability and practicality of intelligent intrusion detection system based on machine learning in high-speed network, an improved fast inductive learning method for intrusion detection (FILMID) is designed and implemented. Accordingly, an efficient intrusion detection model based on FILMID algorithm is presented. The experiment results on the standard testing dataset validate the effectiveness of the FILMID based intrusion detection model.