DDOS attack prevention and validation with metric based ensemble approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-15523-6.pdf
Reference20 articles.
1. Aamir M, Ali Zaidi SM (2021) Clustering based semi-supervised machine learning for DDoS attack classification. J. King Saud Univ. – Comput. Inf. Sci 33(4):436–446. https://doi.org/10.1016/J.JKSUCI.2019.02.003
2. Behal S, Kumar K (2016) Trends in Validation of DDoS Research. Procedia Comput. Sci 85:7–15. https://doi.org/10.1016/J.PROCS.2016.05.170
3. Behal S, Kumar K, Sachdeva M D-FACE: An anomaly based distributed approach for early detection of DDoS attacks and flash events. J. Netw. Comput. App 111:49–63. https://doi.org/10.1016/J.JNCA.2018.03.024
4. Bhuyan MH, Bhattacharyya DK, Kalita JK (2014) Network anomaly detection: Methods, systems and tools. IEEE Commun. Surv. Tutorials 16(1):303–336. https://doi.org/10.1109/SURV.2013.052213.00046
5. Cao Y, Gao Y, Tan R, Han Q, Liu Z (2018) Understanding internet DDoS Mitigation from academic and industrial perspectives. IEEE Access 6:66641–66648. https://doi.org/10.1109/ACCESS.2018.2877710
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