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Controlled Data Sharing for Collaborative Predictive Blacklisting

Freudiger, J; Cristofaro, ED; Brito, A; (2015) Controlled Data Sharing for Collaborative Predictive Blacklisting. In: Almgren, MX and Gulisano, V and Maggi, F, (eds.) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2015. Lecture Notes in Computer Science, vol 9148. (pp. pp. 327-349). Springer: Cham. Green open access

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Abstract

Although sharing data across organizations is often advocated as a promising way to enhance cybersecurity, collaborative initiatives are rarely put into practice owing to confidentiality, trust, and liability challenges. In this paper, we investigate whether collaborative threat mitigation can be realized via a controlled data sharing approach, whereby organizations make informed decisions as to whether or not, and how much, to share. Using appropriate cryptographic tools, entities can estimate the benefits of collaboration and agree on what to share in a privacy-preserving way, without having to disclose their datasets. We focus on collaborative predictive blacklisting, i.e., forecasting attack sources based on one's logs and those contributed by other organizations. We study the impact of different sharing strategies by experimenting on a real-world dataset of two billion suspicious IP addresses collected from Dshield over two months. We find that controlled data sharing yields up to 105% accuracy improvement on average, while also reducing the false positive rate.

Type: Proceedings paper
Title: Controlled Data Sharing for Collaborative Predictive Blacklisting
Event: Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2015. Lecture Notes in Computer Science,
ISBN-13: 978-3-319-20549-6
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-20550-2_17
Publisher version: https://doi.org/10.1007/978-3-319-20550-2_17
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: cs.CR, cs.CR, cs.NI
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1508472
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