@article{MAKHILLJEAS201914917768,
    title = {A Density Maximization-Fuzzy Means Clustering Algorithm for
Network Intrusion Detection},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {9},
    pages = {2964-2974},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.2964.2974},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.2964.2974},
    author = {Ruby and},
    keywords = {DM-FMC,network,dataset,cluster,fuzziness index,Jaipur},
    abstract = {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.}
    }