Adversarially robust and real-time DDoS detection and classification framework using AutoML

Author:

Maurya Sambhrant1,Handa Anand1,Kumar Nitesh1,Shukla Sandeep K.1

Affiliation:

1. C3i Center, Department of Computer Science and Engineering, IIT Kanpur, Kanpur, UP, India

Publisher

Informa UK Limited

Reference65 articles.

1. Abdelaty M. Scott-Hayward S. Doriguzzi-Corin R. & Siracusa D. (2022). Gadot: Gan-based adversarial training for robust ddos attack detection. arXiv. https://arxiv.org/abs/2201.13102

2. Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques

3. Survey on data mining and ip traceback technique in ddos attack;Aggarwal A.;International Journal of Engineering and Computer Science,2015

4. Aiken, J., & Scott-Hayward, S. (2020, March 19). Investigating adversarial attacks against network intrusion detection systems in SDNs. In IEEE conference on network functions virtualization and software defined networks 12/11/2019 → 14/11/2019 dallas, united states. IEEE. Conference on Network Functions Virtualization and Software Defined Networks, IEEE NFV-SDN; Conference date: 12-11-2019 Through 14-11-2019. https://nfvsdn2019.ieee-nfvsdn.org/

5. Alhajjar E. Maxwell P. & Bastian N. D. (2020). Adversarial machine learning in network intrusion detection systems. CoRR Abs/2004.11898. https://arxiv.org/abs/2004.11898

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