Qutaibah Althebyan, Ph.D

Dean, College of Engineering

Al Ain Campus

Education

PhD Computer Science, University of Arkansas – Fayetteville, USA
MSc Computer Science, University of Michigan – Dearborn, USA
BSc System Programming, Jordan University of Science and Technology - Irbed, Jordan

Research Interests

Software security
Cloud computing and cloud security
Insider threat in the cloud
Software engineering especially object – oriented software metrics
Health information system

Selected Publications

R Mohawesh, S Maqsood, Q Althebyan, "Multilingual deep learning framework for fake news detection using capsule neural network", Journal of Intelligent Information Systems 60 (3), 655-671, 2023.

D Mustafa, S Zriqat, IG Mustafa, Qutaibah AlThebyan, “The Impact of Middle and High School Students Participation in the Innovation Nation Competition on Their Education”, 2023 24th International Arab Conference on Information Technology (ACIT), 2023.
Safaa M Khabour, Dheya Mustafa, Qutaibah AlThebyan, “Arabic Sentiment Analysis of Mobile Banking Services Reviews”, 2023 10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023.
D Mustafa, IG Mustafa, S Zriqat, Qutaibah Althebyan, “MIRNA: adaptive 3D game to assist children's distance learning difficulties; design and teachers' intention to use”, International Arab Journal of Information Technology (IAJIT) 20 (3A), 527-535, 2023.

Raed Shatnawi, Qutaibah Althebyan, Baraq Ghaleb, Mohammed Al-Maolegi, “A student advising system using association rule mining”, International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2021.

Mohammad Al-Zinati, Qutaibah Althebyan, Yaser Jararweh, “An Agent-Based Self-Organizing Model for Large-Scale Biosurveillance Systems Using Mobile Edge Computing”, Simulation Modelling Practice and Theory 93, 65-86, 2019, (Impact factor:2.42, Cite Score: 3.58).

Yaseen Q., Jararwah Y. Panda B., Althebyan Q., "An insider threat aware access control for cloud relational databases", Cluster Computing, Vol 20, Issue 3, 2017, pp. 2669–2685, 2017.

Qutaibah Althebyan, Yaser Jararweh, Qussai Yaseen, Mahmoud Al-Ayyoub. “Cloud Support for Large Scale E-Healthcare Systems”. Annals of Telecommunications Journal 2016, Vol 71, Issue 9, pp: 503 – 515, September 2016 [DOI: 10.1007/s12243-016-0496-9], (Impact factor: 0.70).
Qussai Yaseen, Qutaibah Althebyan, Brajendra Panda, Yaser Jararweh. “Mitigating Insider Threat in Cloud Relational Databases”. Journal of Security and Communication Networks, Wiley 2016, Vol 9, pp: 1132 - 1145 [DOI: 10.1002/sec.1405], (Impact factor: 0.72).
Qutaibah Althebyan, Yaser Jararweh, Qussai Yaseen, Omar AlQudah, Mahmoud Al-Ayyoub. “Evaluating Map Reduce Tasks Scheduling Algorithms over Cloud Computing Infrastructure”. Concurrency and Computation: Practice & Experience Journal, Wiley Vol 27, Issue 18, pp: 5686- 5699, July 2015, [DOI: 10.1002/cpe.3595], (Impact factor: 1.0).
Mahmoud Al-Ayyoub, Yaser Jararweh, Mustafa Daraghmeh, Qutaibah Althebyan. “Multi-Agent Based Dynamic Resource Provisioning and Monitoring for Cloud Computing Systems Infrastructure”. Cluster Computing Journal, Springer, March 2015, Vol 18, pp: 919 - 932 [DOI: 10.1007/s10586-015-0449-5], (Impact factor: 1.51).

Teaching Courses

Computer Security Fundamentals
Introduction to Programming
Discrete Structures
Design and Analysis of Algorithms
Security of Information Systems.
Analytics Data Science

Memberships

IEEE

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all.

This person’s work contributes towards the following SDG(s):

A knowledge-base model for insider threat prediction

Published in: A knowledge-base model for insider threat prediction

Jun 20, 2017

Qutaibah Althebyan Brajendra Panda

Many consider insider attacks to be more severe and devastating than outsider attacks. Many techniques exist for defending against outsider attacks. However, little work has been presented for defending insider attacks and threats. In this work, we presented a prediction technique for insider threats. Due to the nature of these kinds of attacks, we relied on some characteristics of the insiders and the decomposition of objects in the underlying system in developing our method.

Full-text Available

Multi-threading based map reduce tasks scheduling

Jan 04, 2014

Qutaibah Althebyan Omar ALQudah Yaser Jararweh Qussai Yaseen

Map Reduce is a parallel and a distributed computing framework used to process datasets that have large scale nature on a cluster. Due to the nature of data that needs to be handled in the Map Reduce problem which involves huge amount of data, many problems came up that are of great importance. Scheduling tasks is considered one of these major problems that face Map Reduce frameworks. In this paper, we tackled this problem and proposed a new scheduling algorithm that is based on a multi-threading principle. In our proposed algorithm, we divided the cluster into multi blocks where each one of them is scheduled by a special thread. Two major factors are used to test our algorithm; the simulation time and the energy consumption. Our proposed scheduler is then compared with existing schedulers and the results showed the superiority and the preference of our proposed scheduler over the existing …

Staff