Q. Dang, Studying machine learning techniques for intrusion detection systems, FDSE, ser. LNCS, vol.11814, pp.411-426, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02306521

Z. He, T. Zhang, and R. B. Lee, Machine learning based ddos attack detection from source side in cloud," in CSCloud, pp.114-120, 2017.

N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, Network intrusion detection for iot security based on learning techniques, IEEE Communications Surveys and Tutorials, vol.21, issue.3, pp.2671-2701, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02500646

T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, KDD, pp.785-794, 2016.

C. Krügel and T. Toth, Using decision trees to improve signature-based intrusion detection, RAID, ser. LNCS, 2003.

N. B. Amor, S. Benferhat, and Z. Elouedi, Naive bayes vs decision trees in intrusion detection systems, SAC, pp.420-424, 2004.

G. Stein, B. Chen, A. S. Wu, and K. A. Hua, Decision tree classifier for network intrusion detection with ga-based feature selection, ACM Southeast Regional Conference, pp.136-141, 2005.

P. A. Resende and A. C. Drummond, A survey of random forest based methods for intrusion detection systems, ACM Comput. Surv, vol.51, issue.3, pp.1-48, 2018.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, NIPS, 2012.

R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-nemrat et al., Deep learning approach for intelligent intrusion detection system, IEEE Access, 2019.

W. Wang, Y. Sheng, J. Wang, X. Zeng, X. Ye et al., HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection, IEEE Access, 2018.

Z. Chiba, N. Abghour, K. Moussaid, A. E. Omri, and M. Rida, Intelligent and improved self-adaptive anomaly based intrusion detection system for networks, IJCNIS, vol.11, issue.2, 2019.

E. Eskin, Anomaly detection over noisy data using learned probability distributions, ICML, pp.255-262, 2000.

M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, Network anomaly detection: Methods, systems and tools, IEEE Communications Surveys and Tutorials, vol.16, issue.1, pp.303-336, 2014.

F. T. Liu, K. M. Ting, and Z. Zhou, Isolation forest, ICDM, pp.413-422, 2008.

Q. Dang, Outlier detection on network flow analysis, CoRR, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01854006

B. Settles, Active learning literature survey, 2009.

A. Ziai, Active learning for network intrusion detection, CoRR, 2019.

G. M. Alqaralleh, M. A. Alshraideh, and A. Alrodan, A comparison study between different sampling strategies for intrusion detection system of active learning model, JCS, 2018.

N. Abe and H. Mamitsuka, Query learning strategies using boosting and bagging, pp.1-9, 1998.

A. Shiravi, H. Shiravi, M. Tavallaee, and A. A. Ghorbani, Toward developing a systematic approach to generate benchmark datasets for intrusion detection, Computers & Security, 2012.

Q. Dang and J. François, Utilizing attack enumerations to study SDN/NFV vulnerabilities, NetSoft. IEEE, pp.356-361, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01763368

Q. Dang and C. Ignat, Computational trust model for repeated trust games, pp.34-41, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01351250