  Recent advances in technology have made our work easier compare to earlier
times. Computer network is growing day by day but while discussing about the
security of computers and networks it has always been a major concerns for
organizations varying from smaller to larger enterprises. It is true that
organizations are aware of the possible threats and attacks so they always
prepare for the safer side but due to some loopholes attackers are able to make
attacks. Intrusion detection is one of the major fields of research and
researchers are trying to find new algorithms for detecting intrusions.
Clustering techniques of data mining is an interested area of research for
detecting possible intrusions and attacks. This paper presents a new clustering
approach for anomaly intrusion detection by using the approach of K-medoids
method of clustering and its certain modifications. The proposed algorithm is
able to achieve high detection rate and overcomes the disadvantages of K-means
algorithm.
