BLOCKCHAIN NETWORK BEHAVIOR-BASED ANOMALY DETECTION

Blockchain technology has the potential to improve the areas of additive manufacturing, supply chain management, and many others within the Navy. An anomaly detection scheme that characterizes blockchain parameters as normal or anomalous using statistical analysis and hierarchical clustering methods was developed in this thesis. The histograms, probability distributions, and boxplots of the data were used to estimate thresholds for outliers that may indicate attacks. The thresholds obtained from dendrograms were used to form clusters and sub-clusters based on the hierarchical data structure; data point indices that do not fall within the threshold are considered anomalous and not included in the clusters. The anomaly detection scheme was implemented in the MATLAB programming environment and validated by successful anomaly detection corresponding to an attack on the public Ethereum blockchain network and in an experimental doorknob-rattling attack on a local blockchain research network. Hierarchical clustering proved to be a more powerful anomaly detection method than statistical analysis methods. http://archive.org/details/blockchainnetwor1094563492 Lieutenant Commander, United States Navy Approved for public release; distribution is unlimited.