capsAEUL: Slow HTTP DoS Attack Detection using Autoencoders through Unsupervised Learning-Reference-Cited by-同舟云学术

capsAEUL: Slow HTTP DoS Attack Detection using Autoencoders through Unsupervised Learning

Published:2021-12-14 Issue: Volume: Page:
ISSN:
Container-title:Asian Internet Engineering Conference
language:
Short-container-title:

Author:

Shaik Tahir Ahmed¹,Kataoka Kotaro¹

Affiliation:

1. IIT Hyderabad, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3497777.3498550

Reference21 articles.

1. Apache. 2020. HTTPD - Apache2 Web Server. Retrieved August 14 2021 from https://ubuntu.com/server/docs/web-servers-apache Apache. 2020. HTTPD - Apache2 Web Server. Retrieved August 14 2021 from https://ubuntu.com/server/docs/web-servers-apache

2. Dor Bank Noam Koenigstein and Raja Giryes. 2021. Autoencoders. arxiv:2003.05991 [cs.LG] Dor Bank Noam Koenigstein and Raja Giryes. 2021. Autoencoders. arxiv:2003.05991 [cs.LG]

3. Canadian Institute for Cybersecurity. 2018. CSE-CIC-IDS2018 on AWS. Retrieved August 14 2021 from https://www.unb.ca/cic/datasets/ids-2018.html Canadian Institute for Cybersecurity. 2018. CSE-CIC-IDS2018 on AWS. Retrieved August 14 2021 from https://www.unb.ca/cic/datasets/ids-2018.html

4. Canadian Institute for Cybersecurity. 2018. Intrusion Detection Evaluation Dataset (CIC-IDS2017). Retrieved August 14 2021 from https://www.unb.ca/cic/datasets/ids-2017.html Canadian Institute for Cybersecurity. 2018. Intrusion Detection Evaluation Dataset (CIC-IDS2017). Retrieved August 14 2021 from https://www.unb.ca/cic/datasets/ids-2017.html

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