Outlier detection with K nearest neighbor clustering

A server which serves wireless network needs strong security systems. For this aim, a new perspective to network security is won by using data mining paradigms like outlier detection, clustering and classification. This study uses k-Nearest Neighbor algorithm for both firstly clustering and then classification. K- NearestNeighbor algorithm needs data warehouse which impersonates user profiles to cluster. Therefore, requested time intervals and requested IPs with text mining are used for user profiles. Users in the network are clustered by calculating optimum k and threshold parameters of k-Nearest Neighbor algorithm with a new approach. Finally, over these clusters, new requests are separated as outlier or normal bydifferent threshold values with different priority weight values and average similarities with different priority weight values.