Evaluating intrusion sensitivity allocation with supervised learning in collaborative intrusion detection-Reference-Cited by-同舟云学术

Evaluating intrusion sensitivity allocation with supervised learning in collaborative intrusion detection

Published:2020-09-10 Issue: Volume: Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Li Wenjuan¹²^ORCID,Tian Fei³,Li Jin¹,Xiang Yang¹

Affiliation:

1. Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong China

2. Department of Computing The Hong Kong Polytechnic University Hong Kong China

3. Research Center Telecom Institute Shenzhen China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5957

Reference46 articles.

1. MengY. The practice on using machine learning for network anomaly intrusion detection. Paper presented at: Proceedings of the 2011 International Conference on Machine Learning and Cybernetics (ICMLC). Guilin China: IEEE;2011:576‐581; IEEE.

2. Taxonomy and Survey of Collaborative Intrusion Detection

3. WuYS FooB MeiY BagchiS. Collaborative intrusion detection system (CIDS): a framework for accurate and efficient IDS. Paper presented at: Proceedings of the ACSAC. Las Vegas NV USA;2003:234‐244.

4. FungCJ BoutabaR. Design and management of collaborative intrusion detection networks. Paper presented at: Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management IM. Ghent Belgium;2013:955‐961.

Cited by 2 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. Insider Intrusion Detection Techniques: A State-of-the-Art Review;Journal of Computer Information Systems;2023-02-14

2. Enhancing Collaborative Intrusion detection networks against insider attack using supervised learning technique;2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon);2022-10-16