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Intrusion Detections in Collaborative Organizations by Preserving Privacy

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Advances in Knowledge Discovery and Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 292))

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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.

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Verma, N., Trousset, F., Poncelet, P., Masseglia, F. (2010). Intrusion Detections in Collaborative Organizations by Preserving Privacy. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00580-0_14

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  • DOI: https://doi.org/10.1007/978-3-642-00580-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00579-4

  • Online ISBN: 978-3-642-00580-0

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