Refined Detection of SSH Brute-Force Attackers Using Machine Learning-Reference-Cited by-同舟云学术

Refined Detection of SSH Brute-Force Attackers Using Machine Learning

Published:2020 Issue: Volume: Page:49-63
ISSN:1868-4238
Container-title:ICT Systems Security and Privacy Protection
language:
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Author:

Hynek Karel,Beneš Tomáš,Čejka Tomáš,Kubátová Hana

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58201-2_4

Reference28 articles.

1. Lecture Notes in Computer Science;AR Abdou,2016

2. AbuseIPDB making the internet safer, one IP at a time, October 2019. https://www.abuseipdb.com/

3. Anderson, B., McGrew, D.: Identifying encrypted malware traffic with contextual flow data. In: ACM Workshop on Artificial Intelligence and Security (2016)

4. Anderson, B., McGrew, D., Perricone, P., Hudson, B.: Joy - a package for capturing and analyzing network flow data and intraflow data, October 2019. https://github.com/cisco/joy

5. Lecture Notes in Computer Science;T Cejka,2015

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