Intrusion Detections in Collaborative Organizations by Preserving Privacy

Verma Nischal 1 François Trousset 2 Pascal Poncelet 3 Florent Masseglia 4
1 IIT Guwahati - Indian Institute of Technology Guwahati
2 LGI2P - Laboratoire de Génie Informatique et Ingénierie de Production
3 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
Abstract : To overcome the problem of attacks on networks, new Intrusion Detection System (IDS) approaches have been proposed in recent years. They consist in identifying signatures of known attacks to compare them to each request and determine whether it is an attack or not. However, these methods are set to default when the attack is unknown from the database of signatures. Usually this problem is solved by calling human expertise to update the database of signatures. However, it is frequent that an attack has already been detected by another organization and it would be useful to be able to benefit from this knowledge to enrich the database of signatures. Unfortunately this information is not so easy to obtain. In fact organizations do not necessarily want to spread the information that they have already faced this type of attack. In this paper we propose a new approach to intrusion detection in a collaborative environment but by preserving the privacy of the collaborative organizations. Our approach works for any signature that may be written as a regular expression insuring that no information is disclosed on the content of the sites.
Keywords : Privacy Intrusion Detection Collaborative Approach
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Submitted on : Monday, November 9, 2009 - 1:57:28 PM
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Verma Nischal, François Trousset, Pascal Poncelet, Florent Masseglia. Intrusion Detections in Collaborative Organizations by Preserving Privacy. Fabrice Guillet and Gilbert Ritschard and Djamel Abdelkader Zighed and Henri Briand. Advances in Knowledge Discovery and Management, 292, Springer, pp.235-247, 2010, Studies in Computational Intelligence, 978-3-642-00579-4. ⟨10.1007/978-3-642-00580-0_14⟩. ⟨lirmm-00430642⟩

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