Evolving Cauchy possibilistic clustering based on cosine similarity for monitoring cyber systems

In this paper the idea of evolving Cauchy clustering based on cosine similarity is given. It is used for monitoring in the case of cyber attacks. The proposed idea is for that kind of processes very interesting because it is very efficient when the data are noisy and when the outliers appear frequently and this is the case when dealing with cyber attacks data. The algorithm is given in an evolving form to be able to deal with big-data sets. One of the important features of the described clustering algorithm is that it deals with just few tuning parameters, such as maximal density. In this paper, the proposed approach was realized on DARPA data base and promising results have been achieved.