Biblio

Maritime transportation plays a critical role for the U.S. and global economies, and has evolved into a complex system that involves a plethora of supply chain stakeholders spread around the globe. The inherent complexity brings huge security challenges including cargo loss and high burdens in cargo inspection against illicit activities and potential terrorist attacks. The emerging blockchain technology provides a promising tool to build a unified maritime cargo tracking system critical for cargo security. However, most existing efforts focus on transportation data itself, while ignoring how to bind the physical cargo movements and information managed by the system consistently. This can severely undermine the effectiveness of securing cargo transportation. To fulfill this gap, we propose a binding scheme leveraging a novel digital identity management mechanism. The digital identity management mechanism maps the best practice in the physical world to the cyber world and can be seamlessly integrated with a blockchain-based cargo management system.

Bitcoin, a peer-to-peer payment system and digital currency, is often involved in illicit activities such as scamming, ransomware attacks, illegal goods trading, and thievery. At the time of writing, the Bitcoin ecosystem has not yet been mapped and as such there is no estimate of the share of illicit activities. This paper provides the first estimation of the portion of cyber-criminal entities in the Bitcoin ecosystem. Our dataset consists of 854 observations categorised into 12 classes (out of which 5 are cybercrime-related) and a total of 100,000 uncategorised observations. The dataset was obtained from the data provider who applied three types of clustering of Bitcoin transactions to categorise entities: co-spend, intelligence-based, and behaviour-based. Thirteen supervised learning classifiers were then tested, of which four prevailed with a cross-validation accuracy of 77.38%, 76.47%, 78.46%, 80.76% respectively. From the top four classifiers, Bagging and Gradient Boosting classifiers were selected based on their weighted average and per class precision on the cybercrime-related categories. Both models were used to classify 100,000 uncategorised entities, showing that the share of cybercrime-related is 29.81% according to Bagging, and 10.95% according to Gradient Boosting with number of entities as the metric. With regard to the number of addresses and current coins held by this type of entities, the results are: 5.79% and 10.02% according to Bagging; and 3.16% and 1.45% according to Gradient Boosting.