NDLI: A peer-to-peer collaborative intrusion detection system

Content Provider	IEEE Xplore Digital Library
Author	Zhou, C.V. Karunasekera ,. S. Leckie, C.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Software Eng., Melbourne Univ., Carlton, Vic., Australia (Zhou, C.V.; Karunasekera , S.; Leckie, C.)
Abstract	Many types of network intrusions occur in multiple networks simultaneously, for example, scanning, worms, and denial-of-service attacks. Most of the current intrusion detection systems work in isolation to detect these attacks. Past research has shown that collaboration between these networks to share suspicious information is an effective way to detect intrusion. However, there are some challenges associated with the idea of collaborative detection, such as scalability and avoidance of a central point of failure. We propose a peer-to-peer approach for collaborative intrusion detection to address these challenges. Our solution proposes secure data sharing between participants from different organizations using a content based peer-to-peer publish/subscribe mechanism. The proposed scheme improves scalability, while avoiding a central point of failure. Our experimental results show improved detection latency and effective load balancing compared to a centralized architecture.
File Size	1902441
File Format	PDF
ISBN	1424400007
ISSN	15312216
DOI	10.1109/ICON.2005.1635451
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-16
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Peer to peer computing Collaboration Intrusion detection Collaborative work Computer worms Computer crime Scalability Computer architecture Computer science Software engineering
Content Type	Text
Resource Type	Article

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