An intelligent behavioral-based DDOS attack detection method using adaptive time intervals

Author:

Shamekhi Ali,Shamsinejad Babaki Pirooz,Javidan Reza

Publisher

Springer Science and Business Media LLC

Reference34 articles.

1. Mahjabin T, Xiao Y, Sun G, Jiang W (2017) A survey of distributed denial-of-service attack, prevention, and mitigation techniques. Int J Distrib Sens Netw 13(12)

2. D. Menscher. "Exponential growth in DDoS attack volumes." Google Cloud. https://cloud.google.com/blog/products/identity-security/identifying-and-protecting-against-the-largest-ddos-attacks (accessed December 2022)

3. Wang M, Cui Y, Wang X, Xiao S, Jiang J (2018) Machine learning for networking: workflow, advances and opportunities. IEEE Netw 32(2):92–99

4. Novaes MP, Carvalho LF, Lloret J, Proença ML (2020) Long short-term memory and fuzzy logic for anomaly detection and mitigation in software-defined network environment. IEEE Access 8

5. Jia Y, Zhong F, Alrawais A, Gong B, Cheng X (2020) FlowGuard: An intelligent edge defense mechanism against IoT DDoS attacks. IEEE Internet Things J 7(10)

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