Rashmi Erandika Ratnayake
Rashmi Erandika Ratnayake is currently a Doctoral Student at the School of Computer Science, University College Dublin, Ireland. She holds a B. Sc. (Hons) Degree in Computer Science from University of Peradeniya, Sri Lanka. She has acquired both industrial experience as a Software Engineer and academic experience as a Lecturer. Her primary research interests are in the fields of Cybersecurity, Blockchain technology and Artificial Intelligence.
Publications
ENSURE-6G Kickoff
The ENSURE 6G project officially commenced with an engaging two-day meeting that convened leading researchers, industry experts, and consortium partners to shape the future of
CONFIDENTIAL6G Project Meeting in Dublin
The CONFIDENTIAL6G consortium held a highly successful project meeting on May 22–23, 2024, hosted by NetsLab at University College Dublin (UCD). This gathering marked a
SECURENET 2024
The 2nd Summit on Security and Privacy in Future Mobile Networks (SECURENET 2024) was held on May 5th, 2024, focusing primarily on the security and
Demo: Enabling Trustworthy Cold Chain Logistics through Blockchain and Machine Learning
Internet of Things (IoT) sensors monitor temperature-sensitive goods throughout the supply chain. Nowadays, blockchain is being widely used for traceability, transparency, and immutable storage of
Evaluating Data Trust in Blockchain-Based IoT Systems Using Machine Learning Techniques
The convergence of blockchain and Internet of Things (IoT) has become increasingly prevalent recently, as it addresses challenges such as single point of failure and
Machine Learning for Data Trust Evaluations in Blockchain-Enabled IoT Systems
Recently, there has been a surge of interest surrounding the integration of blockchain with the Internet of Things (IoT), aiming to address IoT’s inherent issues
Trust Management and Bad Data Reduction in Internet of Vehicles Using Blockchain and AI
Blockchain offers cryptographically secure storage for recording transactions. However, one issue with blockchains is the problem of bad data and data reliability, where bad data