@inproceedings{scholars6460,
            year = {2016},
             doi = {10.1109/ICCOINS.2016.7783222},
            note = {cited By 23; Conference of 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125433},
           pages = {248--252},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings},
           title = {Improving K-Means Clustering using discretization technique in Network Intrusion Detection System},
          author = {Tahir, H. M. and Said, A. M. and Osman, N. H. and Zakaria, N. H. and Sabri, P. N. M. and Katuk, N.},
            isbn = {9781509051342},
        keywords = {Classification (of information); Cluster analysis; Clustering algorithms; Computer crime; Errors; Information science; Learning algorithms; Learning systems; Mercury (metal); Network security; Sodium, Anomaly-based intrusion detection; Bayes Classifier; Discretizations; Integrated machines; Intrusion Detection Systems; K-means clustering; K-means clustering techniques; Network intrusion detection systems, Intrusion detection},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010451193&doi=10.1109\%2fICCOINS.2016.7783222&partnerID=40&md5=383faaf69a73ce464237b0cf804bf4f3},
        abstract = {Network Intrusion Detection Systems (NIDSs) have always been designed to enhance and improve the network security issue by detecting, identifying, assessing and reporting any unauthorized and illegal network connections and activities. The purpose of this research is to improve on the existing Anomaly Based Intrusion Detection (ABID) method using K-Means clustering technique as to maximize the detection rate and accuracy while minimizing the false alarm. The problem with outliers may disturb the K-Means clustering process as it might be avoided in the clustering process from mixing with the normal data that make the NIDSs become less accurate. Thus this research aims to improve the performance of the ABID systems that balance the loss of information or ignored data in clustering. An integrated machine learning algorithm using K-Means Clustering with discretization technique and Na{\~A}?ve Bayes Classifier (KMC-D+NBC) is proposed against ISCX 2012 Intrusion Detection Evaluation Dataset. The outcome depicts that the proposed method generates better detection rate and accuracy up to 99.3 and 99.5 respectively and reduces the false alarm to 1.2 with better efficiency of 0.03 seconds time taken to build model. {\^A}{\copyright} 2016 IEEE.}
}