NDLI: Effective acquaintance management for Collaborative Intrusion Detection Networks

Content Provider	IEEE Xplore Digital Library
Author	Fung, C.J. Jie Zhang Boutaba, R.
Copyright Year	2010
Description	Author affiliation: School of Computer Science, University of Waterloo, Canada (Fung, C.J.; Boutaba, R.) \|\| School of Computer Engineering, Nanyang Technological University, Singapore (Jie Zhang)
Abstract	An effective Collaborative Intrusion Detection Network (CIDN) allows distributed Intrusion Detection Systems (IDSes) to collaborate and share their knowledge and opinions about intrusions, to enhance the overall accuracy of intrusion assessment as well as the ability of detecting new classes of intrusions. Towards this goal, we propose a distributed Host-based IDS (HIDS) collaboration system, particularly focusing on acquaintance management where each HIDS selects and maintains a list of collaborators from which they can consult about intrusions. More specifically, each HIDS evaluates both the false positive (FP) rate and false negative (FN) rate of its neighboring HIDSes' opinions about intrusions using Bayesian learning, and aggregates their opinions about intrusions using a Bayesian decision model. Our dynamic acquaintance management algorithm allows each HIDS to effectively select a set of collaborators. We evaluate our system based on a simulated collaborative HIDS network. The experimental results demonstrate the convergence, stability and incentive of our system.
Starting Page	158
Ending Page	165
File Size	758021
Page Count	8
File Format	PDF
ISBN	9781424489107
e-ISBN	9781424489091
e-ISBN	9781424489084
DOI	10.1109/CNSM.2010.5691316
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Peer to peer computing Heuristic algorithms Collaboration Intrusion detection Maintenance engineering Random variables
Content Type	Text
Resource Type	Article

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