Misc,
BAD: Blockchain Anomaly Detection
(2018)cite arxiv:1807.03833.
Abstract
Anomaly detection tools play a role of paramount importance in protecting
networks and systems from unforeseen attacks, usually by automatically
recognizing and filtering out anomalous activities. Over the years, different
approaches have been designed, all focused on lowering the false positive rate.
However, no proposal has addressed attacks targeting blockchain-based systems.
In this paper we present BAD: the first Blockchain Anomaly Detection solution.
BAD leverages blockchain meta-data, named forks, in order to collect
potentially malicious activities in the network/system. BAD enjoys the
following features: (i) it is distributed (thus avoiding any central point of
failure), (ii) it is tamper-proof (making not possible for a malicious software
to remove or to alter its own traces), (iii) it is trusted (any behavioral data
is collected and verified by the majority of the network) and (iv) it is
private (avoiding any third party to collect/analyze/store sensitive
information). Our proposal is validated via both experimental results and
theoretical complexity analysis, that highlight the quality and viability of
our Blockchain Anomaly Detection solution.
