This research project aims to advance the state-of-the-art in the modeling and analysis of distributed applications (DApps) on top of blockchain networks in monitoring and identifying user behavior profiles, cost analysis and network performance. In this sense, this research aims to generate knowledge to foster technological innovation in the DApps ecosystem in areas of interest to academia, industry, and governments. Additionally, current research aims to increase people's and organizations' understanding and confidence about the benefits and limitations of blockchain technology.
The achievement of this project involves specific goals that are complementary to each other. Are they:
An analysis of the fees and pending time correlation in Ethereum
Identifying user behavior profiles in ethereum using machine learning techniques
Fighting under-price DoS attack in ethereum with machine learning techniques
Analyzing transaction confirmation in ethereum using machine learning techniques
Analysis of Account Behaviors in Ethereum During an Economic Impact Event