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 blockchainbased systems. In this paper we present BAD: the first Blockchain Anomaly Detection solution. BAD leverages blockchain metadata, 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.
M. Signorini et al. REF propose a Blockchain anomaly detection (BAD) solution exploiting blockchain metadata to collect malicious activity.
BAD: Blockchain Anomaly Detection
ArXiv
ArXiv
Computer Science