Research on dynamic self-learning intrusion detection model
Jiamin Wang · Jisuanji gongcheng yu sheji · 2009
It has a few configuration disadvantages in the current popular intrusion detection system.The dynamic self-learning intrusion detection model DMIDS based on envisaging this configuration disadvantage is brought forward.And the renewal mechanism of the dynamic self-learning normal behavior database is presented.This model overcomes the disadvantage that the traditional static detecting model must relearn over all the old and new examples,even can not relearn because of limited memory size.The proof from the test based on KDD’99 attests this model DMIDS reduce the error ratio effectively comparing with the traditional anomaly detection under the precondition of pledging the rate of detection.