Clustering and detecting network intrusion based on fuzzy algorithms

Abstract EN

Clustering or (cluster analysis) has been widely used in data analysis and pattern recognition.

There are several algorithms for clustering large data sets or streaming data sets, their aims to organize a collection of data items into clusters.

These items are more similar to each other within cluster, and difference than they are in the other clusters.

Three fuzzy clustering algorithms (Fuzzy C-Means, Possibilistic CMeans and Gustafson-Kessel algorithms) were applied using kdd cup 99 data set to classify this data set into 23 classes according to the subtype of attacks.

The same data set were classified into 5 classes according to the type of attacks.

In order to evaluate the performance of the system, we compute the classification rate, detection rate and false alarm rate on this data set.

Finally, the results obtained from the experiments with classification rate 100 % which has not been obtained in any previous work.