Liam Meany
IT and Online Learning Systems Engineer in the School of Electronic Engineering
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ABOUT LIAM
Since August 2019, Liam Meany has been employed as IT and Online Learning Systems Engineer in the School of Electronic Engineering at Dublin City University. Liam joined the staff at DCU in 1991 as Lab Technician. Having studied via the distance education with the university, Liam received a B.Sc. in Information Technology in 1996 and M.Sc. in 2003. As a result, the computer systems that underpin remote learning and associated pedagogy is of specific interest. Liam’s duties within the School include Windows and Linux system administration and as a result, the security of these systems is of particular interest. This has led Liam into joining the Entwine Centre with a particular focus in IoT security and machine learning. As part of his duties, Liam supervises a number of M.Eng projects relating to IoT security and has co-authored several papers on this subject. Since 2012, Liam’s other duties include the delivery of two undergraduate modules within the School, EM113 and EE201. From 2003 to 2012 Liam also delivered four remote undergraduate modules (C1, C2, CT1 & CT2) with the then DCU distance education unit, Oscail.RESEARCH INTERESTS
Machine Learning
Machine learning at the edge
Network Infrastructure
The hardware, software and protocols …
Privacy and Security
Firewalls, intrusion detection, …
Selected Publications
• G. Mullen and L. Meany, "Assessment of buffer overflow based attacks on an IoT operating system", 2019 global IoT summit (GIoTS), 2019.• B. Calatayud and L. Meany, "A comparative analysis of buffer overflow vulnerabilities in high-end IoT devices", 2022 IEEE 12th annual computing and communication workshop and conference (CCWC), 2022.
• S. Donnelly and L. Meany, "A taxonomy of machine learning methodologies used against physical unclonable functions", 2021 IEEE 4th 5G world forum (5GWF), 2021.
• L. Meany, "Spam honey pot research", in EC2ND 2005, pp. 23-32, Springer, 2006.
• L. Meany, "An analysis on a three-month university data corpus", EC2ND 2005, .