Qutaibah Althebyan, Ph.D

Dean, College of Engineering

Al Ain Campus




PhD Computer Science, University of Arkansas – Fayetteville, USA

MSc Computer Science, University of Michigan – Dearborn, USA

Research Interests

My research interests include computer security (especially insider threat management and knowledgebase management), cloud computing and cloud security, insider threat in the cloud, database and database security. I am also interested in software engineering especially object – oriented software metrics. Health information system is a new direction that I am researching especially finding solutions for health problems that are intersected with cloud computing.

Selected Publications

Journal Publications
  1. 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).
  2. Qutaibah AlthebyanYaser JararwehQussai YaseenOmar AlQudahMahmoud 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).
  3. Mahmoud Al-AyyoubYaser JararwehMustafa DaraghmehQutaibah 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).
  4. 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).
  5. Qussai Yaseen, Yaser Jararweh, Brajendra Panda, Qutaibah Althebyan. “An Insider Threat Aware Access Control for Cloud Relational Databases”. Cluster Computing Journal, Springer, March 2017, [DOI: doi: 10.1007/s10586-017-0810-y], (Impact factor: 1.51).
  6. Qutaibah Althebyan, Omar Alqudah, Yaser Jararweh, Qussai Yaseen. “A Scalable Map Reduce Tasks Scheduling: A Threading Based Approach”. International Journal of Computational Science and Engineering (IJCSE), Vol. 14, No. 1, 2017.
  7. Mahmoud Al-AyyoubYaser JararwehMustafa DaraghmehQutaibah Althebyan. “Towards Improving Resource Management in Cloud Systems using a Multi-Agent Framework”. International Journal of Cloud Computing (IJCC), Vol. 5, Nos. 1/2, 2016.
  8. Raed Shatnawi, Qutaibah Althebyan, "An Empirical Study of the Effect of Power Law Distribution on the Interpretation of OO Metrics," ISRN Software Engineering, vol. 2013, Article ID 198937, 2013.


Conference Publications
  1. Hassan Najadat, Qutaibah Althebyan, Abedallah Khamaisehy, Mohammad Al-Saady and Ahmad Al Rawashdeh. “Efficiency Analysis of Health Care Centers Using Data Envelopment Analysis”, In Proceedings of the 4th IEEE International Conference on Computer Science, Computer Engineering, and Education Technologies (CSCEET2017), Beirut, Lebanon April 2017.
  2. Qutaibah Althebyan, Hassan Najadat, Bushra AlZa’areer. “Performance Evaluation for Higher Educational Institutions within Data Envelopment Analysis”. In Proceedings of The 5th International Conference on E-Learning and E-Technologies in Education (ICEEE2016), Malaysia September 6 – 8, 2016, pp: 37 – 43.
  3. Qutaibah Althebyan, Rami Mohawesh, Qussai Yaseen and Yaser Jararweh. “Mitigating Insider Threats in a Cloud Using a Knowledgebase Approach while Maintaining Data Availability”. In Proceedings of the 10th International Conference for Internet Technology and Secured Transactions (ICITST-2015), London, UK, December 2015, pp: 226 – 231.
  4. Rami Mohawesh, Qutaibah Althebyan, Qussai Yaseen and Yaser Jararweh. “A Knowledge Base Insider Threat Prevention Model in a Cloud Data Center”. In the Proceedings of the 3rd International IBM Cloud Academy Conference (ICA CON 2015), Budapest, Hungary, May 21 – 23, 2015.
  5. Qutaibah Althebyan, Omar AlQudah, Yaser Jararweh, Qussai Yaseen “Evaluating Map Reduce Tasks Scheduling Algorithms over Virtualized Infrastructure”. In the Proceedings of the 2nd International IBM Cloud Academy Conference (ICA CON 2014), Atlanta, Georgia USA, May 8 – 9, 2014.
  6. Mahmoud Al-Ayyoub, Mustafa Daraghmeh, Yaser Jararweh and Qutaibah Althebyan. “Multi-Agent Based Dynamic Resource Provisioning and Monitoring In Cloud Computing Systems”. In the Proceedings of the 2nd International IBM Cloud Academy Conference (ICA CON 2014), Atlanta, Georgia USA, May 8 – 9, 2014.
  7. Qutaibah Althebyan, Omar AlQudah, Yaser Jararweh, Qussai Yaseen “Multi-Threading Based Map Reduce Tasks Scheduling”. In the Proceedings of the 5th International Conference on Information and Communication Systems (ICICS 2014), April, 2014, Jordan.
  8. Qussai Yaseen, Qutaibah Althebyan, Yaser Jararweh, “PEP-Side Caching: An Insider Threat Port”. The 14th IEEE International Conference on Information Reuse and Integration (IRI 2013) San Francisco, California, USA, August, 2013.
  9. Raed Shatnawi, Qutaibah Althebyan, Baraq Ghalib, Mohammad Molaji “Building A Smart Academic Advising System using Association Rule Mining”, 4th International Conference on Information and Communication Systems (ICICS 2013), Jordan, 2013.
  10. Nahla Shatnawi, Qutaibah Althebyan, Wail Mardini. “Detection of Insiders Misuse in Database Systems” The International MultiConference of Engineers and Computer Scientists (IME, Mar 2011), Hong Kong, March 2011.
  11. Qutaibah Althebyan, Brajendra Panda. “Performance Analysis of an Insider Threat Mitigation Model”. In Proceedings of the 3rd International Conference on Digital Management (ICDIM 2008). University of East London, London UK, 2008.
  12. Qutaibah Althebyan, Brajendra Panda. “A Knowledge-Based Bayesian Model for Analyzing a System after an Insider Attack”. In Proceedings of the 23rd IFIP International Information Security Conference (SEC 2008), Milan, Italy, September 8-10, 2008.[Acceptance Rate = 28%]
  13. Qutaibah Althebyan, Brajendra Panda. “Knowledge Extraction and Management for Insider Threat Mitigation”. In Proceedings of the 6th International Workshop on Security in Information Systems (WOSIS 2008), In Conjunction with ICEIS 2008, Barcelona, Spain, June 12-13, 2008.
  14. Qutaibah Althebyan, Brajendra Panda. “A Knowledge-Base Model for Insider Threat Prediction”. In Proceedings of the 2007 IEEE Workshop on Information Assurance (IAW’07). United States Military Academy, West Point, New York, June 20-22, 2007.

Conference Paper

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.

Conference Paper 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 …