Acknowledgement
This research is funded by the Hanoi University of Science and Technology (HUST) under project number T2020-SAHEP-010. We also thank for the technical contribution of Miss. Nguyen Thuy Linh - our student.
References
- C.E. Brodley, Addressing the selective superiority problem: Automatic algorithm/model class selection, in: Proceedings of the tenth international conference on machine learning, 1993, pp. 17-24.
- P. Brazdil, J. Gama, B. Henery, Characterizing the applicability of classification algorithms using meta-level learning, in: Proc. European Conference on Machine Learning, 1994,
- Ricardo Vilalta, Christophe Giraud-Carrier, Pavel Brazdil, Carlos Soares: Using meta-learning to support data Mining. IJCSA. 1(1), pp.31-45, 2004
- C. Giraud-Carrier, R.Vilalta and P. Brazdil, -Introduction to the special issue on meta-learning‖, Machine Learning 54, 187-193, 2004. https://doi.org/10.1023/B:MACH.0000015878.60765.42
- G. Wang, Q. Song, X. Zhu, An improved data characterization method and its application in classification algorithm recommendation, Appl. Intell. 43 (4) (2015) 892-912. https://doi.org/10.1007/s10489-015-0689-3
- R. Ali, S. Lee, T.C. Chung, Accurate multi-criteria decision making methodology for recommending machine learning algorithm, Expert Syst. Appl. 71 (4) (2017) 257-278 https://doi.org/10.1016/j.eswa.2016.11.034
- S. Gore, N. Pise, Dynamic algorithm selection for data mining classification, Int. J. Sci. Eng. Res. 4 (12) (2013) 2029-2033
- D.H. Wolpert, W.G. Macready: No free lunch theorem for search, Technical Report SFI-TR-05-010, Santa Fe Institute, Santa Fe, NM, 1995
- I. Ullah and Q. H. Mahmoud, A Technique for Generating a Botnet Dataset for Anomalous Activity Detection in IoT Networks, vol. 2020-October, no. April 2021. Springer International Publishing, 2020
- Y. Meidan et al., "N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders," IEEE Pervasive Comput., vol. 17, no. 3, pp. 12-22, 2018, doi: 10.1109/MPRV.2018.03367731.
- Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, "Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection," no. February, pp. 18-21, 2018, doi: 10.14722/ndss.2018.23204.
- I. Vaccari, G. Chiola, M. Aiello, M. Mongelli, and E. Cambiaso, "Mqttset, a new dataset for machine learning techniques on mqtt," Sensors (Switzerland), vol. 20, no. 22, pp. 1-17, 2020, doi: 10.3390/s20226578.
- H. Hindy, E. Bayne, M. Bures, R. Atkinson, C. Tachtatzis, and X. Bellekens, "Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study (MQTT-IoT-IDS2020 Dataset)," Lect. Notes Networks Syst., vol. 180, pp. 73-84, 2021, doi: 10.1007/978-3-030-64758-2_6.
- A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood, and Adna N Anwar, "TON-IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems," IEEE Access, vol. 8, pp. 165130-165150, 2020, doi: 10.1109/ACCESS.2020.3022862.
- N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)," 2015 Mil. Commun. Inf. Syst. Conf. MilCIS 2015 - Proc., 2015, doi: 10.1109/MilCIS.2015.7348942.
- I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset and intrusion traffic characterization," ICISSP 2018 - Proc. 4th Int. Conf. Inf. Syst. Secur. Priv., vol. 2018-January, no. Cic, pp. 108-116, 2018, doi: 10.5220/0006639801080116.
- I. Sharafaldin, A. H. Lashkari, S. Hakak, and A. A. Ghorbani, "Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy," Proc. - Int. Carnahan Conf. Secur. Technol., vol. 2019-October, no. Cic, 2019, doi: 10.1109/CCST.2019.8888419.
- A. H. Lashkari, G. D. Gil, M. S. I. Mamun, and A. A. Ghorbani, "Characterization of tor traffic using time based features," ICISSP 2017 - Proc. 3rd Int. Conf. Inf. Syst. Secur. Priv., vol. 2017-January, no. January, pp. 253-262, 2017, doi: 10.5220/0006105602530262.
- Bill Fulkerson (1995) Machine Learning, Neural and Statistical Classification, Technometrics, 37:4, 459, DOI: 10.1080/.1995.10484383
- Neelam Agarwalla et al, "Deep Learning using Restricted Boltzmann Machines" in International Journal of Computer Science and Information Technologies, Vol.7(3), 2016, 1552-1556