M. Kim, H. Kong, S. Hong, S. Chung, and J. W. Hong, A flow-based method for abnormal network traffic detection, Network Operations and Management Symposium (NOMS). IFIP/IEEE, 2004.

M. Sheikhan and Z. Jadidi, Flow-based anomaly detection in high-speed links using modified gsa-optimized neural network, Neural Computing and Applications, vol.24, issue.3, pp.599-611, 2014.

L. Dolberg, J. François, and T. Engel, Efficient Multidimensional Aggregation for Large Scale Monitoring, Large Installation System Administration Conference (LISA), 2012.
URL : https://hal.archives-ouvertes.fr/hal-00784953

S. E. Coull, F. Monrose, and M. Bailey, On measuring the similarity of network hosts: Pitfalls, new metrics, and empirical analyses, Network and Distributed System Security Symposium, 2011.

S. Lagraa and J. François, Knowledge discovery of port scans from darknet, Symposium on Integrated Network and Service Management (IM). IFIP/IEEE, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01636215

T. T. Nguyen and G. Armitage, A survey of techniques for internet traffic classification using machine learning, Communications Surveys Tutorials, vol.10, issue.4, pp.56-76, 2008.

A. W. Moore and K. Papagiannaki, Toward the accurate identification of network applications, Passive and Active Network Measurement, C. Dovrolis, 2005.

P. Velan, M. ?ermák, P. ?eleda, and M. Dra?ar, A survey of methods for encrypted traffic classification and analysis, International Journal of Network Management, vol.25, issue.5, pp.355-374, 2015.

A. K. Michael, E. Valla, N. S. Neggatu, and A. W. Moore, Network traffic classification via neural networks, 2017.

W. D. Donato, A. Pescape, and A. Dainotti, Traffic identification engine: an open platform for traffic classification, Network, vol.28, issue.2, pp.56-64, 2014.

J. Zhang, X. Chen, Y. Xiang, W. Zhou, and J. Wu, Robust network traffic classification, Transactions on Networking, vol.23, issue.4, 2015.

L. Grimaudo, M. Mellia, E. Baralis, and R. Keralapura, Select: Selflearning classifier for internet traffic, Transactions on Network and Service Management, vol.11, issue.2, pp.144-157, 2014.

M. H. Bhuyan, K. Bhattacharyya, and J. K. Kalita, Surveying port scans and their detection methodologies, The Computer Journal, vol.54, pp.1565-1581, 2011.

C. B. Lee, C. Roedel, and E. Silenok, Detection and characterization of port scan attacks

Z. Durumeric, E. Wustrow, and J. A. Halderman, Zmap: Fast internetwide scanning and its security applications, Conference on Security. USENIX Association, 2013.

M. Coudriau, A. Lahmadi, and J. Francois, Topological Analysis and Visualisation of Network Monitoring Data: Darknet case study, International Workshop on Information Forensics and Security (WIFS)
URL : https://hal.archives-ouvertes.fr/hal-01403950

A. Dhabi, , 2016.

C. Fachkha and M. Debbabi, Darknet as a source of cyber intelligence: Survey, taxonomy, and characterization, Communications Surveys Tutorials, vol.18, issue.2, pp.1197-1227, 2016.

E. Balkanli, J. Alves, and A. N. Zincir-heywood, Supervised learning to detect ddos attacks, Computational Intelligence in Cyber Security (CICS), 2014 IEEE Symposium on, pp.1-8, 2014.

P. Mell and R. Harang, Limitations to threshold random walk scan detection and mitigating enhancements, Communications and Network Security (CNS), pp.332-340, 2013.

J. Jung, V. Paxson, A. W. Berger, and H. Balakrishnan, Fast portscan detection using sequential hypothesis testing, Security and Privacy. iEEE, pp.211-225, 2004.

, Fast portscan detection using sequential hypothesis testing, Security and Privacy, pp.211-225, 2004.

P. Chen, L. Desmet, and C. Huygens, A study on advanced persistent threats, Communications and Multimedia Security, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01404186