Prof. Taher Alzahrani | Cybersecurity | Best Researcher Award

Assistant Professor at Imam Muhammad Ibn Saud Islamic University (IMSIU), Saudi Arabia.

Dr. Taher Alzahrani is a distinguished cybersecurity expert, IT consultant, and academic leader with over 22 years of experience in the field of computer science, cybersecurity, and network systems. He is the founder and partner of SCS, a cybersecurity firm based in Riyadh, Saudi Arabia, and currently serves as an Assistant Professor at Imam University’s College of Computer and Information Sciences. His expertise spans complex information networks, cybersecurity strategies, risk assessment, IT governance, and big data analytics. With a strong academic and professional background, Dr. Alzahrani has played a pivotal role in implementing national and international cybersecurity frameworks, consulting on high-profile IT projects, and conducting advanced research in cybersecurity and network security.

Professional Profile:

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Education Background

Dr. Alzahrani holds a Doctor of Philosophy (Ph.D.) in Computer Science from RMIT University, Australia, awarded in 2016. His doctoral research focused on Intrusion Detection Systems (IDS) and the detection of community structures in bipartite networks. Prior to this, he earned a Master of Information Security and Assurance from RMIT University in 2011 and a Master of Business Administration from Training and Consulting Group, Australia, in 2012. He also holds a Network Specialist for E-Government certification from Okinawa International Center, Japan (2007), and a Bachelor’s Degree in Computer Science from King Abdulaziz University, Jeddah, obtained in 2002.

Professional Development

Dr. Alzahrani has accumulated extensive experience across various sectors, including government, finance, and academia. His career began as a Computers Supervisor at the Saudi embassies in Athens and Tirana (2002–2004), followed by his role as a Programs Developer at the Ministry of Finance’s National Center for Financial and Economic Information in Riyadh (2004–2008). He later served as an IT Consultant, Network Administrator, and Cybersecurity Information Specialist at the same organization from 2008 to 2019. In 2018, he founded a cybersecurity firm, SCS, which specializes in security solutions, risk assessments, and IT consulting. Since 2019, he has been an Assistant Professor at Imam University, where he teaches and researches cybersecurity, IT governance, and network security.

Research Focus

Dr. Alzahrani’s research spans multiple domains, including cybersecurity strategies, complex network systems, IT governance, risk management, information security, and big data analytics. His work emphasizes secure communication, cryptography, ethical hacking, secure e-commerce, and governance, risk, and compliance (GRC) platforms. His contributions extend to cybersecurity awareness programs and frameworks such as ISO/IEC 27001, ISO/IEC 20000-1, NCA, CITC, and SAMA frameworks.

Author Metrics:

Dr. Alzahrani is a well-recognized researcher and publisher in the field of cybersecurity and network security. His research employs computational analysis and parallelization to address large-scale cybersecurity problems. He has published several scientific papers on complex networks, information security policies, and big data analysis. Additionally, he is an active contributor to cybersecurity discussions and knowledge dissemination through social media and professional forums.

Honors & Awards

Dr. Alzahrani has received multiple certifications and recognitions throughout his career. He is a Certified International Cybersecurity Expert, recognized for his expertise in complex networks, risk assessment, decision-making, and cybersecurity strategies. He has also been honored for his contributions as a trainer and consultant in cybersecurity, IT governance, and ethical hacking. His achievements include leading cybersecurity implementations for government and corporate entities, ensuring compliance with national and international security frameworks.

Publication Top Notes

1. Community Detection in Bipartite Networks: Algorithms and Case Studies

• Authors: Taher Alzahrani and K. J. Horadam
• Published In: Chapter in “Complex Systems and Networks: Dynamics, Controls, and Applications”
• Publication Date: 2015
• Pages: 25–50
• Summary: This chapter surveys recent advancements in community detection within bipartite networks. The authors focus on two prominent algorithms for unipartite networks—the modularity-based Louvain method and the flow-based Infomap—and discuss their adaptations for bipartite structures. They apply these algorithms to four projected networks of varying sizes and complexities, concluding that Infomap’s clusters better represent the inherent community structures in bipartite networks compared to those identified by the Louvain method.
• Access: Available through Springer:
• link.springer.com

2. Community Detection in Bipartite Networks Using Random Walks

• Authors: Taher Alzahrani, K. J. Horadam, and Serdar Boztas
• Published In: Proceedings of the 5th Workshop on Complex Networks (Complex Networks V)
• Publication Date: 2014
• Pages: 157–165
• Summary: Addressing the limitations of modularity-based community detection algorithms in bipartite networks, this paper proposes integrating a projection method based on common neighbor similarity into the Infomap algorithm. This integration allows for effective clustering of weighted one-mode networks derived from bipartite structures. The authors demonstrate the efficacy of this approach on four real bipartite networks, showing that the random walks technique surpasses modularity-based methods in accurately detecting communities.
• Access: Available through Springer:
• link.springer.com

3. Analysis of Two Crime-Related Networks Derived from Bipartite Social Networks

• Authors: Taher Alzahrani and K. J. Horadam
• Published In: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• Publication Date: 2014
• Pages: 890–897
• Summary: This study analyzes two crime-related networks derived from bipartite social structures. By projecting bipartite networks into unipartite forms, the authors apply community detection algorithms to uncover hidden structures within criminal networks, providing insights into the organization and interactions among individuals involved in criminal activities.
• Access: Available through IEEE Xplore.

4. Finding Maximal Bicliques in Bipartite Networks Using Node Similarity

• Authors: Taher Alzahrani and Kathy Horadam
• Published In: Applied Network Science
• Publication Date: 2019
• Pages: 1–25
• Summary: This paper presents a method for identifying maximal bicliques in bipartite networks by leveraging node similarity measures. The approach enhances the understanding of the structural properties of bipartite networks and aids in the discovery of dense substructures within these networks.
• Access: Available through Springer:
• appliednetsci.springeropen.com

5. An Advanced Approach for the Electrical Responses of Discrete Fractional-Order Biophysical Neural Network Models and Their Dynamical Responses

• Authors: Y. M. Chu, Taher Alzahrani, S. Rashid, W. Rashidah, S. ur Rehman, and M. Alkhatib
• Published In: Scientific Reports
• Publication Date: 2023
• Article Number: 18180
• Summary: This research introduces an advanced approach to modeling the electrical responses of discrete fractional-order biophysical neural networks. The study explores the dynamical behaviors of these models, providing insights into their potential applications in understanding neural dynamics.
• Access: Available through Nature:

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