Bayesian decision aggregation in collaborative intrusion detection networks

Cooperation between intrusion detection systems (IDSs) allow collective information and experience from a network of IDSs to be shared for improving the accuracy of detection. A critical component of a collaborative network is the mechanism of feedback aggregation in which each IDS makes an overall...

Full description

Saved in:
Bibliographic Details
Published in2010 IEEE Network Operations and Management Symposium - NOMS 2010 pp. 349 - 356
Main Authors Fung, Carol J, Quanyan Zhu, Boutaba, Raouf, Basar, Tamer
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2010
Subjects
Aggregates
Bayesian methods
Collaboration
Collaborative work
Computer networks
Computer worms
Costs
Feedback
Intrusion detection
Peer to peer computing
Online AccessGet full text
ISBN9781424453665
1424453666
ISSN1542-1201
DOI10.1109/NOMS.2010.5488489

Cover

More Information
Summary:Cooperation between intrusion detection systems (IDSs) allow collective information and experience from a network of IDSs to be shared for improving the accuracy of detection. A critical component of a collaborative network is the mechanism of feedback aggregation in which each IDS makes an overall security evaluation based on peer opinions and assessments. In this paper, we propose a collaboration framework for intrusion detection networks (CIDNs) and use a Bayesian approach for feedback aggregation by minimizing the combined costs of missed detection and false alarm. The proposed model is highly scalable, robust, and cost effective. Experimental results demonstrate an improvement in the true positive detection rate and a reduction in the average cost of our mechanism compared to existing models.
ISBN:9781424453665
1424453666
ISSN:1542-1201
DOI:10.1109/NOMS.2010.5488489