A DDOS intrusion detection model based on SSNFF
Yong Xu · Applied science and technology · 2008
A model for intrusion detection based on spatially selection normal flow filtration(SSNFF) is presented to deal with the network attacks such as distributed denial of service (DDOS) signals. It uses SSNFF algorithm to extract DDOS signal. Then a detection for intrusion node is performed using both denoising algorithm based on maximal curve length threshold and step node determination algorithm, thus eliminating the influence of amplitude variations on detection performance in the algorithm based on maximal modulus. Finally a model of DDOS intrusion detection is given. Computer simulations were made on the detection method for a real data set involving the normal web traffic collected from web server plus the DDOS attack flow. Some results are reported with relevant concluding remarks.