In-depth Comparative Evaluation of Supervised Machine Learning Approaches for Detection of Cybersecurity Threats-Reference-Cited by-同舟云学术

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In-depth Comparative Evaluation of Supervised Machine Learning Approaches for Detection of Cybersecurity Threats

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 4th International Conference on Internet of Things, Big Data and Security
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Author:

D’hooge Laurens¹,Wauters Tim¹,Volckaert Bruno¹,De Turck Filip¹

Affiliation:

1. Ghent University - imec, IDLab, Department of Information Technology, Technologiepark-Zwijnaarde 126, Gent and Belgium, --- Select a Country ---

Publisher

SCITEPRESS - Science and Technology Publications

Cited by 10 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. Exploration of Various Machine Learning Techniques for Identifying and Mitigating DDoS Attacks;2023 20th Annual International Conference on Privacy, Security and Trust (PST);2023-08-21

2. Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection;Journal of Cybersecurity and Privacy;2023-04-03

3. A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack;Computer Modeling in Engineering & Sciences;2023

4. Towards Continuous Enrichment of Cyber Threat Intelligence: A Study on a Honeypot Dataset;2022 IEEE International Conference on Cyber Security and Resilience (CSR);2022-07-27

5. Meta-Learning to Improve Unsupervised Intrusion Detection in Cyber-Physical Systems;ACM Transactions on Cyber-Physical Systems;2021-10-31

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