The Detection of Network Intrusion Based on Improved Adaboost Algorithm-Reference-Cited by-同舟云学术

The Detection of Network Intrusion Based on Improved Adaboost Algorithm

Published:2020-01-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 4th International Conference on Cryptography, Security and Privacy
language:
Short-container-title:

Author:

Wang Quanmin¹,Wei Xuan¹

Affiliation:

1. Beijing University of Technology, Beijing, China

Publisher

ACM

Reference16 articles.

1. Mukkamala S. and A.H. Sung Significant Feature Selection Using Computational Intelligent Techniques for Intrusion Detection. 2005. Mukkamala S. and A.H. Sung Significant Feature Selection Using Computational Intelligent Techniques for Intrusion Detection. 2005.

2. Feature deduction and ensemble design of intrusion detection systems

3. Mutual information-based feature selection for intrusion detection systems

4. International Conference on Systems & Informatics. International Conference on Systems & Informatics. M. Qi Y. Chen Y. Zhang Incremental SVM algorithm to intrusion detection base on boundary areas 2012

Cited by 6 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. Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning;Big Data Mining and Analytics;2024-06

2. Network Intrusion Detection Method Based on CNN-BiLSTM-Attention Model;IEEE Access;2024

3. A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things;Information;2023-10-07

4. A Survey on Data-driven Network Intrusion Detection;ACM Computing Surveys;2022-12-31

5. IoT Network Intrusion Detection with Ensemble Learners;2022 13th International Conference on Information and Communication Technology Convergence (ICTC);2022-10-19