Abdullah Mohd Zin
National University of Malaysia
118 Papers
377 Citations
Abdullah Mohd Zin is an academic researcher from National University of Malaysia. The author has contributed to research in topics: Network simulation & Computer science. The author has an hindex of 12, co-authored 117 publications. Previous affiliations of Abdullah Mohd Zin include University of Nottingham.
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Papers
Performance study of selective encryption in comparison to full encryption for still visual images
TL;DR: Experimental results have proven that the selective encryption approach based on edge and face detection can significantly reduce the time of encrypting still visual images as compared to full encryption, and this approach can be considered a good alternative in the implementation of real-time applications that require adequate security levels.
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Ceilidh as a Course Management Support System
TL;DR: Ceilidh is a course-management system developed for teaching computer programming that includes extensive administration and progress monitoring facilities as well as support for other forms of assessment including short-answer marking.
50
Encryption as a Service (EaaS) as a Solution for Cryptography in Cloud
TL;DR: This paper designed an Encryption as a Service in order to get rid of the security risks of cloud provider's encryption and the inefficiency of client-side encryption and developed a private cloud as an intermediary.
46
A parallel membrane inspired harmony search for optimization problems
Ali Maroosi,Ravie Chandren Muniyandi,Elankovan A Sundararajan,Abdullah Mohd Zin +3 more
- 01 Dec 2016
TL;DR: This work introduced a parallel framework based on membrane computing to improve the harmony search and employed some previously proposed approaches to improve standard harmony search by allowing its parameters to be adaptive during the processing steps.
38
•Journal Article
Anomalies classification approach for network-based intrusion detection system
TL;DR: A set of network traffic features that deemed to be the most relevant features in identifying wide range of network anomalies and an A-IDS alarm classifier based on machine learning technologies to automatically classify activities detected by a packet header-based anomaly detection system are presented.