Rate My Professor Lakmal Rupasinghe

Dr. Lakmal Rupasinghe serves as Research Associate in the School of Electrical Engineering, Computing and Mathematical Sciences, Faculty of Science and Engineering at Curtin University. He earned his PhD from Curtin University in 2019 in the same school, with his doctoral thesis titled 'Reinforcement Learning based Trust framework for MANET Environment.' He previously obtained an MBA from the Postgraduate Institute of Management, University of Sri Jayewardenepura, in 2008. With more than two decades of professional experience in research, academia, consulting, and training—having instructed over 20,000 professionals globally—Dr. Rupasinghe concurrently holds the positions of Faculty Research Chair, Senior Lecturer (Higher Grade), and Consultant in the Department of Computer Systems Engineering, Faculty of Computing, Sri Lanka Institute of Information Technology (SLIIT), since January 2003. He leads the Information Security Research Group at SLIIT and supervises MPhil and PhD students. As a chartered IT security professional, he holds CISSP certification from (ISC)² and CISA from ISACA.

Dr. Rupasinghe's research focuses on artificial intelligence and machine learning applications in cybersecurity, including mobile ad-hoc networks (MANETs), trust frameworks, static and dynamic mobile security analysis, network component auditing, machine learning for fintech, blockchain, and smart contracts. His key publications encompass journal articles such as 'Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series' (Journal of Computer Science, 2020) and 'T-AODV Heuristic based Trust implementation in Ad-hoc Routing Protocol for Wireless Sensor Networks' (International Journal of Computer Applications, 2018). He has presented extensively at conferences, with papers on trust evaluation models for online social networks (NITC, 2018), reinforcement learning to enhance trust in MANETs (NITC, 2018), trustworthy MANET routing ESTAODV (ICIAfS, 2018), mobile-based malware detection and classification using ensemble artificial intelligence (ICAC, 2019), and SDN-based security solutions for legislative email communications (ICCCA, 2016). Recent contributions include 'Secured Real-Time Machine Communication Protocol' (MDPI, 2024).