Z. Chen, C. Yeo, B. Francis, and C. Lau, Combining MIC feature selection and feature-based MSPCA for network traffic anomaly detection, 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC), pp.176-181, 2016.
DOI : 10.1109/DIPDMWC.2016.7529385

W. Lin, S. Ke, and C. Tsai, CANN: An intrusion detection system based on combining cluster centers and nearest neighbors, Knowledge-Based Systems, vol.78, 2015.
DOI : 10.1016/j.knosys.2015.01.009

F. G. Jr, L. Carvalho, J. Rodrigues, P. Jr, and . Ml, Network anomaly detection using IP flows with Principal Component Analysis and Ant Colony Optimization, J Netw Comput Appl, vol.64, 2016.

R. Ciplinskas and N. Paulauskas, Outlier Detection Method Use for the Network Flow Anomaly Detection, Mokslas - Lietuvos ateitis, vol.41, issue.3, pp.327-333, 2016.
DOI : 10.1007/978-0-387-09823-4

R. Wankhede and V. Chole, Intrusion Detection System using Classification Technique, International Journal of Computer Applications, vol.139, issue.11, 2016.
DOI : 10.5120/ijca2016909397

URL : http://doi.org/10.5120/ijca2016909397

M. Kontaki, A. Gounaris, and A. Papadopoulos, Continuous monitoring of distancebased outliers over data streams, 2011 IEEE 27th Int. Conf. Data Eng, pp.135-146, 2011.

Y. Purwanto, . Kuspriyanto, . Hendrawan, and B. Rahardjo, Time based anomaly detection using residual polynomial fitting on aggregate traffic statistic, 2015 1st International Conference on Wireless and Telematics (ICWT), pp.1-5, 2015.
DOI : 10.1109/ICWT.2015.7449256

I. Putra, Y. Purwanto, and F. Suratman, Modified K-means algorithm using timestamp initialization in sliding window to detect anomaly traffic, 2015 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), pp.19-23, 2015.
DOI : 10.1109/ICCEREC.2015.7337042

K. Goeschel, Reducing false positives in intrusion detection systems using datamining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis, pp.2016-2017, 2016.

M. Uddin, A. Rehman, and N. Uddin, Signature-based Multi-Layer Distributed Intrusion Detection System using Mobile Agents. 12 Big Data, Cloud and Computing ChallengesFuzzy Based Intrusion Detection Systems in MANET, Procedia Comput Sci, vol.50, pp.109-114, 2012.
DOI : 10.5121/ijnsa.2010.2411

URL : http://airccse.org/journal/nsa/1010ijnsa11.pdf

K. Singh, S. Guntuku, A. Thakur, and C. Hota, Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests, Information Sciences, vol.278, pp.488-497, 2014.
DOI : 10.1016/j.ins.2014.03.066

V. Carela-español, P. Barlet-ros, A. Cabellos-aparicio, and J. Solé-pareta, Analysis of the impact of sampling on NetFlow traffic classification, Computer Networks, vol.55, issue.5, 2011.
DOI : 10.1016/j.comnet.2010.11.002

R. Kumari, . Sheetanshu, and M. Singh, Anomaly detection in network traffic using K-mean clustering, 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), pp.387-393, 2016.
DOI : 10.1109/RAIT.2016.7507933

A. Alsayat and H. Sayed, Social media analysis using optimized K-Means clustering, 2016 IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA), pp.61-66, 2016.
DOI : 10.1109/SERA.2016.7516129

T. Velmurugan and D. , Efficiency of k-Means and K-Medoids Algorithms for Clustering Arbitrary Data Points, ResearchGate, vol.3, pp.1758-1764, 2012.

J. Asmuss and G. Lauks, Network traffic classification for anomaly detection fuzzy clustering based approach, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp.313-318, 2015.
DOI : 10.1109/FSKD.2015.7381960

A. Abid, A. Kachouri, and A. Mahfoudhi, Anomaly detection through outlier and neighborhood data in Wireless Sensor Networks, 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp.26-30, 2016.
DOI : 10.1109/ATSIP.2016.7523045

P. Fu and X. Hu, Biased-sampling of density-based local outlier detection algorithm, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp.1246-1253, 2016.
DOI : 10.1109/FSKD.2016.7603357

Z. Tsiatsikas, A. Fakis, and D. Papamartzivanos, Battling against DDoS in SIP: Is Machine Learning-based detection an effective weapon? In: 2015 12th Int, pp.301-308, 2015.

B. Gajic, S. Nováczki, and S. Mwanje, An improved anomaly detection in mobile networks by using incremental time-aware clustering, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp.1286-1291, 2015.
DOI : 10.1109/INM.2015.7140483

P. Wong, D. Haglin, and D. Gillen, A visual analytics paradigm enabling trillionedge graph exploration, 2015 IEEE 5th Symp. Large Data Anal. Vis. LDAV, pp.57-64, 2015.
DOI : 10.1109/ldav.2015.7348072

B. Li, J. Springer, G. Bebis, H. Gunes, and M. , A survey of network flow applications, Journal of Network and Computer Applications, vol.36, issue.2, pp.567-581, 2013.
DOI : 10.1016/j.jnca.2012.12.020

H. Zheng, C. Li, and Z. Chen, Petri Nets Based Modeling and Analysis of UPnP Security Ceremonies, 2011 Third Pacific-Asia Conference on Circuits, Communications and System (PACCS), 2011.
DOI : 10.1109/PACCS.2011.5990235