TY  - JOUR
T1  - A Density Maximization-Fuzzy Means Clustering Algorithm for
Network Intrusion Detection
AU - , Ruby AU - Chaurasia, Sandeep 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 9
SP  - 2964
EP  - 2974
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.2964.2974
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.2964.2974
KW  - DM-FMC
KW  -network
KW  -dataset
KW  -cluster
KW  -fuzziness index
KW  -Jaipur
AB  - Detecting intrusions from the network traffic dataset is one of the demanding and critical task in
recent days. This study aims to develop a Density Maximization-Fuzzy Means Clustering (DM-FMC) algorithm
for identifying the intrusions from the network traffic datasets. In this process, the raw datasets are
preprocessed at the initial stage for removing the irrelevant attributes and to normalize the data for further use.
Based on the values of threshold, density and fuzziness index, the cluster is formed by using the DM-FMC
technique. In the end, the cluster is categorized to efficiently identify the anomalies from the dataset.
ER  -