Identifying the general pattern of the academic computer networks based on users daily behaviors

Abstract

The use of the internet has become wide spread with the developments in technology as a result of this data has been removed to electronic environment. With the increase of data stored in the electronic environment, the security of the data has become much important. For this reason, network anomalies and attacks should be detected early. There are many different data mining methods used to detect network anomalies. In this study general behavior of academic networks determined to detect network anomalies. For this purpose, a network state analysis method using Iterative K-Means and Profile Hidden Markov Model (PHMM) methods is proposed.