Visualize Network Anomaly Detection by Using K-Means Clustering Algorithm
Alaa Mohamed Riad, Ibrahim Elhenawy, Ahmed E. Hassan, Nancy Awadallah · International journal of Computer Networks & Communications · 2013
With the ever increasing amount of new attacks in today's world the amount of data will keep increasing, and because of the base-rate fallacy the amount of false alarms will also increase.Another problem with detection of attacks is that they usually isn't detected until after the attack has taken place, this makes defending against attacks hard and can easily lead to disclosure of sensitive information.In this paper we choose K-means algorithm with the Kdd Cup 1999 network data set to evaluate the performance of an unsupervised learning method for anomaly detection.The results of the evaluation showed that a high detection rate can be achieve while maintaining a low false alarm rate .This paper presents the result of using k-means clustering by applying Cluster 3.0 tool and visualized this result by using TreeView visualization tool .