A New Method for Filtering IDS False Positives with Semi-supervised Classification-Reference-Cited by-同舟云学术

A New Method for Filtering IDS False Positives with Semi-supervised Classification

Published:2012 Issue: Volume: Page:513-519
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Zhang Minghua,Mei Haibin

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-31588-6_66.pdf

Reference11 articles.

1. Lecture Notes in Computer Science;T. Pietraszek,2004

2. Lecture Notes in Computer Science;M.S. Shin,2004

3. Bildoy, J., Clausen, S., Klausen, T.E.: Classifying Alerts in Multi-tier Intrusion Detection Systems. Master, Agder University College, Arendal (2004)

4. Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised Learning. MIT Press, Cambridge (2006)

5. Fatima, L.S., Mezrioui, A.: Improving the Quality of Alerts with Correlation in Intrusion Detection. International Journal of Computer Science and Network Security 7(12) (2007)

Cited by 5 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. A Survey on the Applications of Semi-supervised Learning to Cyber-security;ACM Computing Surveys;2024-06-22

2. Dual-stage intrusion detection for class imbalance scenarios;Computer Fraud & Security;2019-01

3. A General and Expandable Insider Threat Detection System Using Baseline Anomaly Detection and Scenario-Driven Alarm Filters;2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE);2018-08

4. MVPSys : Toward practical multi-view based false alarm reduction system in network intrusion detection;Computers & Security;2016-07

5. Intrusion Detection Using Disagreement-Based Semi-supervised Learning: Detection Enhancement and False Alarm Reduction;Cyberspace Safety and Security;2012