Network Intrusion Detection Based on Dynamic SOFM
Guo Hong-yan · Computers & Security · 2009
Considering current intrusion detection system with high misinformation rate and low detection rate,this paper applies dynamic Self- Organizing Feature Map(SOFM)neural network to intrusion detection , it has defined clustering node trust degree , owing to competing result、 trust degree、the centre similarity degree. it can work out the node addition and deletion tractics, raise clustering result.we use the KDD99 data collection to carry out an experiment,the result indicates systematically under keeping low condition of misuse rate , intrusion detecting rates improve to some extent.