Research

Research Interest:

Internet of Things (IoT)
Utilizing machine learning in the Internet of Things (IoT) domain
Smart city-enabling technologies
Utilizing machine learning for cybersecurity
Network Intrusion Detection Systems
Data-driven networks

More precisely, I am working on the following topics:

Current Research Projects:

Investigating the Utilization of Machine Learning Algorithms in Predicting Cyberattacks
Applying Machine Learning for Watch-list Filtering in Anti-money Laundering

Publications:

Journal Papers:-

R. Alwhoush, M. Qutqut, and S. Oteafy, Air Quality Monitoring Framework for Smart Buildings Using Edge Computing and Fuzzy Logic Techniques, Journal of Systems Science and Information, Submitted, 2024.
A. Alshaikh Qasem, M. Qutqut, F. Alhaj, and A. Kitana, SRFE: A Stepwise Recursive Feature Elimination Approach for Network Intrusion Detection, Peer-to-Peer Networking and Applications Journal, Aug 2024.
S. Ahmad, F. Almasalha, M. Qutqut, and M. Hijjawi, Centralized Smart Energy Monitoring System for Legacy Home Appliances, Journal of Energy Informatics, Vol. 7, No. 29, 2024.
S. Murrar, F. Alhaj, and M. Qutqut, Machine Learning Algorithms for Transportation Mode Prediction: A Comparative Analysis, International Journal of Computing and Informatics (Informatica), Vol. 48, No. 6, 2024.
M. Hijjawi, M. Shinwan, M. Qutqut, W. Alomoush, et al., Improved flat mobile core network architecture for 5G mobile communication systems, International Journal of Data and Network Science, Vol. 7, No. 3, pp. 1421–1434, 2023
M. Alkhalili, M. Qutqut, and F. Almasalha, Investigation of Applying Machine Learning for Watch-List Filtering in Anti-Money Laundering, IEEE Access, Vol. 9, pp. 18481–18496, 2021.
A. Shaban, F. Almasalha, and M. Qutqut, Hybrid User Action Prediction System for Automated Home using Association Rules and Ontology, IET Wireless Sensor Systems Journal, Vol. 9, No. 2, pp. 85-93, 2019.
Y. Rasheed, M. Qutqut and F. Almasalha, Overview of the Current Status of NoSQL Database, IJCSNS International Journal of Computer Science and Network Security, Vol. 19, No. 4, pp. 47–53, 2019.
R. Watheq, F. Almasalha, and M. Qutqut, A New Steganography Technique using JPEG Images, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 11, pp. 751–760, 2018.
M. Qutqut, A. Al-Sakran, F. Almasalha, and H. S. Hassanein, Comprehensive Survey of the IoT Open-Source OSs, IET Wireless Sensor Systems, Vol. 8, No. 6, pp. 323–339, 2018.
M. Al-Janabi, M. Qutqut, and M. Hijjawi, Machine Learning Classification Techniques for Heart Disease Prediction: A Review, International Journal of Engineering and Technology (UAE), Vol. 7, No. 4, pp. 5558-5564, 2018.
M. Hamoudy, M. Qutqut, and F. Almasalha, Video Security in the Internet of Things (IoT): An Overview, IJCSNS International Journal of Computer Science and Network Security, Vol. 17, No. 8, pp. 199–205, 2017.
M. Qutqut, M. Feteiha, and H. S. Hassanein, “Performance Analysis of Mobile Small Cells over LTE-A Networks”, IET Communications, Dec 2016.

Book Chapter:-

M. Qutqut and H. S. Hassanein, Mobility Management in Femtocell Networks, in Future Wireless Networks: Architecture, Protocols, and Services, edited by M. Guizani, Hsiao-Hwa Chen, and C. Wang, CRC Press, 2015.

Conference Papers:

S. Murrar, M. Qutqut, and F. Alhaj, Identifying Travel Modes Using GPS Trajectories: A Short Review, Accepted for the 6th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), July 2024.
M. Alkayed, F. Almasalha, M. Hijjawi, and M. Qutqut, Factors Analysis Affecting Academic Achievement of Undergraduate Student: A study on Faculty of Information Technology Students at Applied Science Private University, In Proc. of 2023 International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, Mar 2023, pp. 1–12.
M. Majthoub, M. Qutqut, and Y. Odeh, Software Re-engineering: An Overview, In Proc. of 8th International Conference on Computer Science and Information Technology (CSIT), Amman, Jordan, July 2018, pp. 266-270.
A. Al-Sakran, M. Qutqut, F. Almasalha, H. S. Hassanein, and M. Hijjawi, An Overview of the Internet of Things Closed Source Operating Systems, in Proc. of 14th International Wireless Communications and Mobile Computing Conference (IWCMC), June 2018, Limassol, Cyprus, pp. 291-297.
M. Qutqut, H. Abou-zeid, H.S. Hassanein, and F. Al-Turjman, “Dynamic Small Cell Placement Strategies for LTE Heterogeneous Networks,” in Proc. of the IEEE Symposium on Computers and Communications (ISCC), Funchal, Madeira, Portugal, June 2014, pp. 1-6.
M. Qutqut, M. Feteiha, and H. S. Hassanein, “Outage Probability Analysis of Mobile Small cells over LTE-A Networks”, in Proc. of the International Wireless Communications & Mobile Computing Conf. (IWCMC), Nicosia, Cyprus, Aug. 2014, pp. 1045-1050.
M. Feteiha and M. Qutqut and H. Hassanein, “Pairwise Error Probability Evaluation of Cooperative Mobile Femtocells”, in Proc. of the IEEE Global Communications (GLOBECOM), Atlanta, USA, Dec. 2013, pp. 4588–4593.
M. Qutqut, F. Al-Turjman, and H. Hassanein, “HOF: A History-based Offloading Framework for LTE Networks Using Mobile Small Cells and Wi-Fi”, in Proc. of the IEEE Local Computer Networks (LCN), Sydney, Australia, 2013, pp. 77-83.
M. Qutqut, F. Al-Turjman, and H. Hassanein, “MFW: Mobile Femtocells utilizing WiFi: A data offloading framework for cellular networks using mobile femtocells ”, in Proc. of the IEEE International Conf. on Communications (ICC), Budapest, Hungary, 2013, pp. 5020-5024.

Non-refereed Publications:

M. Qutqut and H. S. Hassanein, ” Dynamic Small Cell Placement Strategies for HetNets”, (poster) in the 5th Annual WiSense Workshop, Ottawa, Ontario, Aug 29-30, 2014.
M. Qutqut and H. S. Hassanein, "Mobility Management in Wireless Broadband Femtocells," School of Computing, Queen's University, Technical Report 2012-590, July 2012.
M. Qutqut and F. Al-Turjman, “MFW: Mobile Femtocells Utilizing WiFi” (poster) in the fourth annual Queen’s Graduate Computing Society Conference (QGCSC), Kingston, ON, 8-9 May 2013.

Current Research Projects:

Investigating Several Feature Elimination Approaches for Network Intrusion Detection Systems

Details will be added later.

A. Alshaikh Qasem, M. Qutqut, F. Alhaj, and A. Kitana, SRFE: A Stepwise Recursive Feature Elimination Approach for Network Intrusion Detection, Peer-to-Peer Networking and Applications Journal, Aug 2024.

Investigation of Applying Machine Learning for Watch-List Filtering in Anti-Money Laundering

Financial institutions must meet international regulations to ensure they do not serve criminals and terrorists. They also need to monitor financial transactions to detect suspicious activities continuously. Businesses have many operations that monitor and validate their customers' information against sources that either confirm their identities or disprove them. Failing to detect unclean transaction(s) will result in harmful consequences for the financial institution responsible for that, such as warnings or fines, depending on the transaction severity level. The financial institutions use anti-money laundering (AML) software sanctions screening and watch-list filtering to monitor every transaction within the financial network and verify that none of the transactions can be used to do business with forbidden people. Lately, the financial industry and academia have agreed that machine learning (ML) may significantly impact monitoring money transaction tools to fight money laundering. Several research works and implementations have been done on Know Your Customer (KYC) systems, but there is no work on the watch-list filtering systems because of compliance risks. Thus, we propose an innovative model to automate the process of checking blocked transactions in the watch-list filtering systems. To the best of our knowledge, this project is the first research work on automating the watch-list filtering systems. We propose to develop a Machine Learning - Component (ML-component) that will be integrated with the current watch-list filtering systems. Our proposed ML component will have three phases: monitoring, advising, and taking action. Our model will handle a known critical issue: the false positives (i.e., transactions blocked by a false alarm). Also, it minimizes the compliance officers' effort and provides faster processing time. We will perform several experiments using different ML algorithms (SVM, DT, and NB).

M. Alkhalili, M. Qutqut, and F. Almasalha, Investigation of Applying Machine Learning for Watch-List Filtering in Anti-Money Laundering, IEEE Access, Vol. 9, pp. 18481 - 18496, 2021.

Previous Research Projects:

A comprehensive Study of the IoT OSs

The Internet of Things (IoT) has attracted a great deal of research and industry attention recently and is envisaged to support diverse emerging domains including smart cities, health informatics, and smart sensory platforms. Operating system (OS) support for IoT plays a pivotal role in developing scalable and interoperable applications that are reliable and efficient. IoT is implemented by both high-end and low-end devices that require OSs. Recently, we have witnessed a diversity of OSs emerging into the IoT environment to facilitate IoT deployments and developments. In this project, we study the common and existing open-source and closed-source OSs for IoT to provide a comprehensive overview. Each OS is described in detail based on a set of designing and developmental aspects that we established. We present a taxonomy of the current IoT open-source OSs. The objective of this project is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT device/application based on their functional and non-functional requirements.

M. Qutqut, A. Al-Sakran, F. Almasalha, H. S. Hassanein, Comprehensive Survey of the IoT Open-Source OSs, IET Wireless Sensor Systems, Vol. 8, No. 6, pp. 323-339, 2018.
A. Al-Sakran, M. Qutqut, F. Almasalha, H. S. Hassanein, and M. Hijjawi, An Overview of the Internet of Things Closed Source Operating Systems, in Proc. 14th International Wireless Communications and Mobile Computing Conference (IWCMC), June 2018, Limassol, Cyprus, pp. 291-297.
M. Hamoudy, M. Qutqut, and F. Almasalha, Video Security in the Internet of Things (IoT): An Overview, IJCSNS International Journal of Computer Science and Network Security, Vol. 17, No. 8, pp 199-205, 2017.

Hybrid User Action Prediction System for Automated Home using Association Rules and Ontology

Nowadays, with the rapid increase in the number of Internet users, Internet services dominate a primary part of our lifestyle. Moreover, the evolution of the Internet of Things (IoTs) has introduced new insights into smart platforms and devices that lead to the new vision of ‘smart homes’. The idea of smart homes is not a recent concept; it has been in high interest by both academia and industry to make smart homes more convenient technology for human comfort. In this project, we propose a new hybrid prediction system based on the frequent pattern (FP)-growth and ontology graphs for home automation systems. The proposed system simulates human prediction actions by adding common sense data and utilizing the advantages of the ontology graph and the FP-growth to find a better solution for automated systems predicting home user actions. Two ontology graphs are introduced with FP growth to evaluate the proposed system to achieve the best results. Both graphs are tested through multiple weight values with the results of FP-growth.

A. Shaban, F. Almasalha, and M. Qutqut, Hybrid User Action Prediction System for Automated Home using Association Rules and Ontology, IET Wireless Sensor Systems Journal, Vol. 9, No. 2, pp. 85 – 93, 2019.

Dynamic Placement Strategies for Outdoor Small Cells

Recently, several cellular operators have started outdoor deployments of small cells to enhance service in high-dense areas (e.g., downtown areas). In this regard, we assess and propose HetNet solutions that capitalize on SBS deployments to boost capacity and coverage under varying scenarios. Initially, we investigate the core challenge of SBS placement in high-demand outdoor zones. We propose dynamic placement strategies (DPS) for SBSs, and present two models that optimize placement while minimizing service delivery cost when feasibility is the core challenge, and minimizing macrocells utilization as their deployment, compared to small cells, poses a constant challenge. Both problems are formulated as Mixed Integer Linear Programs (MILPs). These solutions are contrasted with two greedy schemes which we have presented and evaluated over extensive simulations. Our simulation results demonstrate that our proposed DPS achieves significant reductions in service delivery cost.

M. Qutqut, H. Abou-zeid, H.S. Hassanein, and F. Al-Turjman, “Dynamic Small Cell Placement Strategies for LTE Heterogeneous Networks,” in Proc. of the IEEE Symposium on Computers and Commun. (ISCC), Madeira, Portugal, June, 2014.

Analyzing the Performance Gains of Mobile Small Cells

In this project, we aimed to study the impact of mobile cell deployments in public transit vehicles by quantifying the potential performance gains. We consider an SBS mounted in a public transit bus (i.e., mobSBS) to serve onboard users with a transmitter on the roof to communicate with MBSs. UEs communicate with the nearby mobSBSs (deployed in a bus) instead of distant MBSs. The mobSBS communicates with the MBSs through a wireless backhaul link. The mobSBS aggregates users' traffic to and from the MBS. An illustration of the considered system scenario is presented in the following Figure.

We specifically choose to study Pairwise Error Probability (PEP) and Outage Probability (OP) performance metrics as they are considered important indicators in assessing UE's QoS and power consumption. In addition to PEP and OP, we examine the achievable performance gains from enabling aggregation through mobSBS regarding diversity gain and distance advantage. We provide analytical and simulation results to evaluate the performance gains of such a deployment. Our results indicate that significant diversity gains are achievable, and error and outage rates are tremendously reduced.

We propose to deploy an appropriate precoder at the mobSBS in the vehicle to overcome the degraded performance of the received signal in outdoor wireless links. Precoded transmission helps extract the underlying rich multi-path Doppler diversity inherited in this double-selective fading link.
We derive tight-bound closed-form expressions for PEP and OP to act as benchmarks to assess our analysis and future studies.
We analytically and through simulation demonstrate the performance gains of mobile small cell deployment and compare them with the typical transmission scenario.

M. Qutqut, M. Feteiha, and H. S. Hassanein, “Outage Probability Analysis of Mobile Small cells over LTE-A Networks”, in Proc. of the International Wireless Communications & Mobile Computing Conf. (IWCMC), Nicosia, Cyprus, Aug. 2014.
M. Feteiha and M. Qutqut and H. Hassanein, “Pairwise Error Probability Evaluation of Cooperative Mobile Femtocells”, in Proc. of the IEEE Global Communications (GLOBECOM), Atlanta, USA, Dec. 2013, pp. 4588-4593.

Data Offloading Framework Using Mobile Small Cells and Urban WiFi

In this project, we propose a novel offloading framework that allows mobile operators to offload a portion of data traffic generated by mobile users in public transit vehicles by using mobile small cells and city-wide WiFi. We propose to utilize mobile small cells in our framework based on their ability to improve cellular performance demonstrated in the previous project.

This framework aims to offload users's data that is intended to be transferred through macrocells using WiFi. We propose to deploy SBSs onboard public transit vehicles (mobSBSs) as in the previous project. In addition, mobSBSs have WiFi transmitter(s) that are installed on the rooftop of the vehicle to provide backhaul access for the mobSBS by connecting to urban WiFi Access Points (APs) (which are widely used and already cover many urban cities). Hence, routing mobile data traffic to the operator's CN, through WiFi, to relieve overburdened macrocells.

Further, we incorporate WiFi coverage maps and users' service history profile in the proposed offloading framework in order to make the offloading process more efficient. As a result, the traffic on the cellular network is reduced and gets geared towards WiFi networks. Our simulation results show that our framework is able to boost the amount of offloaded data traffic from the macrocells while maintaining appropriate levels of MBSs and mobSBSs utilization. In addition, it shows a significant enhancement in terms of total offloaded traffic in comparison to typical offloading approaches in which users' service history was not considered. To the best of our knowledge, this is the first mention of a framework utilizing WiFi as a backhaul for mobile small cells towards off loading macrocells data traffic.

M. Qutqut, F. Al-Turjman, and H. Hassanein, “HOF: A History-based Offloading Framework for LTE Networks Using Mobile Small Cells and Wi-Fi”, in Proc. of the IEEE Local Computer Networks (LCN) workshops, Sydney, Australia, 2013, pp. 77-83.
M. Qutqut, F. Al-Turjman, and H. Hassanein, “MFW: Mobile Femtocells utilizing WiFi: A data offloading framework for cellular networks using mobile femtocells ”, in Proc. of the IEEE International Conf. on Communications (ICC), Budapest, Hungary, 2013, pp. 5020-5024.

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