On the Performance, Utility Improvements, and Illicit Transaction Detection of Blockchain Networks

Abstract

The success of Bitcoin gave rise to the use of blockchain technology. Until recently, blockchains were commonly deployed as cryptocurrencies. Now, with the rapid development of smart contracts, blockchain technology is finding new applications. Two main types of blockchain, i.e., public blockchains and permissioned blockchains, were formed. Nonetheless, a number of issues were overlooked with the fast development of blockchain technology. Existing blockchain networks and their applications often assume perfect connectivity, leaving the use of blockchains with connection restriction unaddressed. Moreover, these applications are often deployed on independent networks, resulting in the formation of isolated data silos. In addition, various financial crimes have also been found on cryptocurrency networks. This thesis aims to fill the existing gaps related to the performance, utility improvements and anomaly detection of blockchain networks. Firstly, we develop mathematical models for transaction processing and block propagation and conduct an emulation to study the network behaviours under network disturbances. We show node churns do not affect the transaction throughput, but may cause temporary inconsistencies. We also show block propagation can be modelled with a modified epidemic-spreading model which incorporates network effects. Secondly, we design and implement two proxy-based architectures to improve the utility of blockchains. We first propose a blockchain-based architecture to deliver banking services to regions with an intermittent connectivity to the broader Internet. We next develop a proxybased framework to enable trusted data transfer between independent permissioned blockchains. Different from existing interoperability schemes on public blockchains, the proposed framework allows the destination requester to select a set of peers on the source network to prove the integrity of the transferred data. Thirdly, we investigate the detection of laundering transactions on the Bitcoin network. We analyse laundering and regular transactions from a graph-theoretic perspective, and apply manual feature extraction and graph representation learning techniques to build classifiers for binary classification between laundering and regular transactions. We show that random-walk based embeddings are effective in binary classification. We then apply the proposed classifiers to predict unknown laundering transactions,