A. Milenkoski, M. Vieira, S. Kounev, A. Avritzer, and B. D. Payne, Evaluating computer intrusion detection systems: A survey of common practices, ACM Comput. Surv, vol.48, issue.1, p.41, 2015.

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.

K. Yang, J. Ren, Y. Zhu, and W. Zhang, Active learning for wireless iot intrusion detection, IEEE Wireless Commun, vol.25, issue.6, pp.19-25, 2018.

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.

M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, A detailed analysis of the KDD CUP 99 data set, CISDA, pp.1-6, 2009.

J. Mchugh, Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory, ACM Transactions on Information and System Security (TISSEC), vol.3, issue.4, pp.262-294, 2000.

M. V. Mahoney and P. K. Chan, An analysis of the 1999 darpa/lincoln laboratory evaluation data for network anomaly detection, International Workshop on Recent Advances in Intrusion Detection, pp.220-237, 2003.

A. Özgür and H. Erdem, A review of KDD99 dataset usage in intrusion detection and machine learning between, PeerJ PrePrints, vol.4, p.1954, 2010.

A. Shiravi, H. Shiravi, M. Tavallaee, and A. A. Ghorbani, Toward developing a systematic approach to generate benchmark datasets for intrusion detection, Computers & Security, vol.31, issue.3, pp.357-374, 2012.

I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, Toward generating a new intrusion detection dataset and intrusion traffic characterization, ICISSP, pp.108-116, 2018.

Q. Dang, Studying machine learning techniques for intrusion detection systems, Future Data and Security Engineering, pp.411-426, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02306521

C. Zhang and Y. Ma, Ensemble machine learning: methods and applications, 2012.

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

X. Li and N. Ye, Decision tree classifiers for computer intrusion detection, Journal of Parallel and Distributed Computing Practices, vol.4, issue.2, pp.179-190, 2001.

C. Krügel and T. Toth, Using decision trees to improve signature-based intrusion detection, RAID, ser, vol.2820, pp.173-191, 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.

R. R. Reddy, Y. Ramadevi, and K. V. Sunitha, Effective discriminant function for intrusion detection using SVM," in ICACCI, pp.1148-1153, 2016.

D. Bhamare, T. Salman, M. Samaka, A. Erbad, and R. Jain, Feasibility of supervised machine learning for cloud security, CoRR, 2018.

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, pp.1106-1114, 2012.

A. A. Diro and N. Chilamkurti, Distributed attack detection scheme using deep learning approach for internet of things, Future Generation Comp. Syst, vol.82, pp.761-768, 2018.

R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-nemrat et al., Deep learning approach for intelligent intrusion detection system, IEEE Access, vol.7, pp.41-525, 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, vol.6, pp.1792-1806, 2018.

S. D. Antón, S. Sinha, and H. D. Schotten, Anomaly-based intrusion detection in industrial data with SVM and random forests, SoftCOM, pp.1-6, 2019.

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

J. H. Friedman, Greedy function approximation: a gradient boosting machine, pp.1189-1232, 2001.

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, vol.14, issue.8, pp.1155-1173, 2018.

D. D. Lewis and W. A. Gale, A sequential algorithm for training text classifiers, SIGIR, pp.3-12, 1994.

H. S. Seung, M. Opper, and H. Sompolinsky, Query by committee, COLT, pp.287-294, 1992.

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

B. Settles, M. Craven, and S. Ray, Multiple-instance active learning, NIPS, pp.1289-1296, 2007.

J. O'neill, S. J. Delany, and B. M. Namee, Model-free and model-based active learning for regression, UKCI, ser. Advances in Intelligent Systems and Computing, vol.513, pp.375-386, 2016.

A. Burkov, Machine Learning Engineering. LeanPub, 2019.

R. K. Deka, D. K. Bhattacharyya, and J. K. Kalita, Active learning to detect ddos attack using ranked features, Computer Communications, vol.145, pp.203-222, 2019.

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