On Collaborative Predictive Blacklisting
Luca Melis, Apostolos Pyrgelis, Emiliano De Cristofaro

TL;DR
This paper evaluates current collaborative predictive blacklisting techniques, revealing that increased collaboration raises false positives, and proposes a hybrid privacy-preserving approach to improve accuracy and privacy balance.
Contribution
The paper provides a measurement study of existing CPB systems and introduces a hybrid approach that balances collaboration benefits with privacy and accuracy considerations.
Findings
Collaboration increases predicted attacks but also false positives.
Current systems have poor accuracy due to high false positives.
The hybrid approach improves the trade-off between true and false positives.
Abstract
Collaborative predictive blacklisting (CPB) allows to forecast future attack sources based on logs and alerts contributed by multiple organizations. Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives. Moreover, sharing alerts is often hindered by confidentiality, trust, and liability issues, which motivates the need for privacy-preserving approaches to the problem. In this paper, we present a measurement study of state-of-the-art CPB techniques, aiming to shed light on the actual impact of collaboration. To this end, we reproduce and measure two systems: a non privacy-friendly one that uses a trusted coordinating party with access to all alerts (Soldo et al., 2010) and a peer-to-peer one using privacy-preserving data sharing (Freudiger et al., 2015). We show that,…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Blockchain Technology Applications and Security · Cloud Data Security Solutions
