(Short Paper) Effectiveness of Entropy-Based Features in High- and Low-Intensity DDoS Attacks Detection-Reference-Cited by-同舟云学术

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(Short Paper) Effectiveness of Entropy-Based Features in High- and Low-Intensity DDoS Attacks Detection

Published:2019 Issue: Volume: Page:207-217
ISSN:0302-9743
Container-title:Advances in Information and Computer Security
language:
Short-container-title:

Author:

Koay Abigail,Welch Ian,Seah Winston K. G.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-26834-3_12

Reference14 articles.

1. Bhuyan, M.H., Bhattacharyya, D., Kalita, J.: E-LDAT: a lightweight system for DDoS flooding attack detection and IP traceback using extended entropy metric. Secur. Commun. Netw. 9(16), 3251–3270 (2016)

2. Gu, Y., McCallum, A., Towsley, D.: Detecting anomalies in network traffic using maximum entropy estimation. In: Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, p. 32. USENIX Association (2005)

3. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)

4. Jun, J.H., Ahn, C.W., Kim, S.H.: DDoS attack detection by using packet sampling and flow features. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 711–712. ACM (2014)

5. Kottler, S.: February 28th DDoS Incident Report (2018). https://githubengineering.com/ddos-incident-report/

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1. A Deep Learning-based System for DDoS Attack Anticipation;2022 IEEE Latin-American Conference on Communications (LATINCOM);2022-11-30

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