University Events

Description: Topic: Dependability Modeling and Analysis of Blockchain-Based Systems

Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A

Zoom Conference Link: https://umassd.zoom.us/j/98092685237
Meeting ID: 980 9268 5237 Passcode: 494029

Abstract:
The blockchain technology has immense potential in diverse applications, such as cryptocurrencies, financial services, smart contracts, supply chains, healthcare, and energy trading. Due to the business-critical and/or safety-critical nature of these applications, it is pivotal to model and evaluate the dependability of blockchain-based systems, contributing to their reliable and robust design and operation.

In this dissertation, potential risks to the blockchain-based systems are examined. Impacts of critical parameters like block size, block interval, stale block rate on the system performance are investigated through case studies. We then model and analyze the dependability of Bitcoin, a peer-to-peer cryptocurrency system built upon the blockchain technology that enables an individual user to trade freely without involving any intermediate agents. Three attack models are considered, including the Eclipse attack that aims to monopolize the information flow of the victim node, the selfish mining attack that aims to collect unfair mining rewards by intentionally withholding blocks, and the 51% attack that aims to control over 50 percent of the network nodes for gaining the power to alter the blockchain. Analytical methods based on continuous-time Markov chains, semi-Markov processes, and multi-valued decision diagram are investigated for the dependability analysis of Bitcoin nodes and networks subject to attacks. Effects of several model parameters related to the miner's habits in system protection, restart, and mining frequency, time to restart, time to detect and delete the malicious message, as well as parameters reflecting computing power and attack triggers of selfish miners and recovery capabilities of honest miners are examined. This dissertation also contributes by proposing two defensive strategies: the dynamic difficulty adjustment algorithm and the acceptance limitation policy. Case studies show that both strategies can effectively discourage dishonest selfish miners and improve the overall dependability and resilience of the system against selfish mining attacks.

Advisor(s): Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

Committee Members: Dr. Honggang Wang, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Yuzhu (Julia) Li, Professor, Department of Decision and Information Sciences, UMASS Dartmouth; Dr. Jun Guo, Professor, College of Software, Northeastern University, China

NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.

*For further information, please contact Dr. Liudong Xing at 508.999.8883 or via email at liudong.xing@umassd.edu.