Clustering based on Self-Organizing Ant Colony Networks with Application to Intrusion Detection

Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. A clustering model based on self-organizing ant colony networks (CSOACN) is systematically proposed for intrusion detection system. Instead of using the linear segmentation function of the CSI model, here we propose to use a nonlinear probability conversion function and can help to solve linearly inseparable problems. Using a set of benchmark data from 1998 DARPA, we demonstrate that the efficiency and accuracy of CSOACN