Detection of Brute-Force Attacks in End-to-End Encrypted Network Traffic

Author:

Wichmann Pascal1,Marx Matthias1,Federrath Hannes1,Fischer Mathias1

Affiliation:

1. Universität Hamburg, DE

Funder

German Federal Ministry of Transport and Digital Infrastructure

Publisher

ACM

Reference37 articles.

1. Lessons learned from the deployment of a high-interaction honeypot

2. A comparison of unsupervised learning techniques for encrypted traffic identification;Bacquet Carlos;Journal of Information Assurance and Security,2010

3. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Discriminative spatial-temporal feature learning for modeling network intrusion detection systems;Journal of Computer Security;2024-02-02

2. A study on Automated Cyberattacks Detection and Visualization;2022 14th International Conference on Computational Intelligence and Communication Networks (CICN);2022-12-04

3. Network Intrusion Detection Systems: A Systematic Literature Review o f Hybrid Deep Learning Approaches;International Journal of Emerging Science and Engineering;2022-06-30

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