Network Intrusion Feature Selection Based on Fast Attribute Reduction
Qi Mu, Gong Shang-fu, Xiaoru Bi, She Xiang-yang · Jisuanji gongcheng · 2011
Aiming to problem that independent and redundant attributes of high dimensional network data cause classification algorithms' slow detection speed and low detection rate in network intrusion detection,this paper presents a feature selection method for network intrusion based on fast attribute reduction.It adopts Mutual Information(MI) between condition and label attributes of network data as measure to discard independent attributes,then a formula for measuring attribute importance based on positive region of rough set is applied as heuristic information to design a fast attribute reduction algorithm,which removes redundant attributes of network data to realize optimal selection of feature subset of network intrusion.Simulation experiment is done in KDDCUP1999.Result shows that the method is more effective in discarding independent and redundancy attributes and it has higher intrusion detection rate and lower false positive rate.