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Real-time alert correlation with type graphs

Tedesco, Gianni; Aickelin, Uwe

Authors

Gianni Tedesco gxt@cs.nott.ac.uk

Uwe Aickelin uwe.aickelin@nottingham.ac.uk



Contributors

R Sekar
Editor

Arun K. Pujari
Editor

Abstract

The premise of automated alert correlation is to accept that
false alerts from a low level intrusion detection system are inevitable and use attack models to explain the output in an understandable way. Several algorithms exist for this purpose which use attack graphs to model the ways in which attacks can be combined. These algorithms can be classified in to two broad categories namely scenario-graph approaches,
which create an attack model starting from a vulnerability assessment and type-graph approaches which rely on an abstract model of the relations between attack types. Some research in to improving the efficiency of type-graph correlation has been carried out but this research has ignored the hypothesizing of missing alerts. Our work is to present a novel type-graph algorithm which unifies correlation and hypothesizing in to a single operation. Our experimental results indicate that the approach is
extremely efficient in the face of intensive alerts and produces compact output graphs comparable to other techniques.

Publication Date Jan 1, 2008
Peer Reviewed Peer Reviewed
Issue 5352
Series Title Lecture notes in computer science
Book Title Information systems security: 4th international conference, ICISS 2008, Hyderabad, India, December 16-20, 2008: proceedings /
ISBN 9783540898627
APA6 Citation Tedesco, G., & Aickelin, U. (2008). Real-time alert correlation with type graphs. In R. Sekar, & A. K. Pujari (Eds.), Information systems security: 4th international conference, ICISS 2008, Hyderabad, India, December 16-20, 2008: proceedings /Springer
Publisher URL http://www.springer.com/computer/communication+networks/book/978-3-540-89861-0
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information The original publication is available at www.springerlink.com

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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