InfoSec Seminar: Controlled Data Sharing for Collaborative Predictive Blacklisting

Speaker: Dr Emiliano de Cristofaro

Date/Time: 01-Jan-1970, 00:00 UTC

Venue:

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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.

Joint work with Julien Freudiger and Alex Brito (PARC) to appear at DIMVA, July 8-9, 2015

Full version: http://arxiv.org/pdf/1502.05337.pdf

This page was last modified on 27 Mar 2014.