An intrusion detection method combined Rough Sets and data mining

In order to improve the detection efficiency of intrusion detection and reduce false alarm, an approach combined rough sets and data mining is proposed to enhance the traditional intrusion detection methods. First, collected data is classified and preprocessed by normalizing the value variables and discretely processing the nominal variables, an attribute reduction to the result set can be made based on Pawlak attribute weights Rough Set Algorithm with characteristics of the property up and down approximation set. According to attribute reduction, association rules can be generated which satisfy a certain degree of confidence, then imported into the rule set. Experiments show that the detection approach combined rough sets and data mining has improved the detection efficiency above 20%. The detection rate is almost linear with the number of intrusions.