Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk
Bitcoin Security currency economics cybercrime exchanges
Blockchain challenges and opportunities: a survey
This is the first comprehensive survey on the blockchain technology in both technological and application perspectives. This paper focuses on the state-of-art blockchain studies and gives a thorough view of the blockchain technology rather than concentrates on a specific kind of cryptocurrency. It's certain that beginners of blockchain will benefit a lot from this writing.
Blockchain Survey
T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis
This paper proposed a novel Temporal Weighted Multidigraph (TWMDG) method to perform node embedding on Ethereum transaction network. TWMDG incorporated temporal and weighted information of financial transaction networks into node embeddings and obtain better experimental results against baselines on two tasks: link prediction and node classification. It was the first work to understand Ethereum transaction records via graph embedding.
Transaction Graph Ethereum data mining
Practical Byzantine Fault Tolerance
Today there is a growing number of malicious attacks and software errors, which will certainly cause faulty nodes to exhibit Byzantine behavior. The paper describes the first state-machine replication protocol that correctly survives Byzantine faults in asynchronous networks, and also provides experimental results that quantify the cost of the replication technique.
Blockchain Security
Who Spent My EOS? On the (In)Security of Resource Management of EOS.IO
A well understanding of potential threats and their underlying causes is necessary when building a secure system. This paper is the first study that analyzes the security of the EOS.IO system and points out that all of the security vulnerabilities they find stem from the EOS.IO design, which is beneficial to the whole EOS ecosystem.
Security Smart Contract EOS
Exploiting Blockchain Data to Detect Smart Ponzi Schemes on Ethereum
This paper proposes a machine learning method to detect smart Ponzi schemes. The ground truth data is obtained by manually checking the source code. And two categories of features, account features and code features, are extracted. Based on the features and ground truth data, a random forest model is built and applied to identify latent smart Ponzi schemes. The authors estimate that there are more than 500 smart Ponzi schemeson Ethereum.
Blockchain Smart Contract Ethereum