Oluomachi Ejiofor | Cybersecurity | Best Paper Award

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Ms. Oluomachi Ejiofor | Cybersecurity | Best Paper Award

Cybersecurity Analyst at Austin peay state University, United States

Ms. Oluomachi Ejiofor is a cybersecurity researcher, academic, and AI specialist with expertise in network security, cryptography, cyber threat intelligence, and financial fraud detection. She holds an M.S. in Computer Science from Austin Peay State University (2023) and a B.S. in Information Science from Abia State University (2013).

Currently, she serves as a Graduate Teaching Fellow at Austin Peay State University, where she instructs and mentors students in computer science and cybersecurity. Previously, she worked as a Graduate Research Assistant and Cyber Analyst, contributing to AI-enhanced security models and data privacy solutions.

Her research focuses on machine learning applications in cybersecurity, AI-driven fraud detection, privacy in wireless sensor networks, and blockchain security. She has authored multiple high-impact publications on fraud detection, financial cybersecurity, and cyber threat analytics, with over 400 citations.

Ms. Ejiofor is an active member of IEEE, the Nigeria Computer Society (NCS), and the International Society of Data Science. She also holds several cybersecurity certifications and has contributed to developing AI-based security frameworks for fraud detection and healthcare data protection.

Publication Profile

Google Scholar

Educational Details

  • M.S. in Computer Science (2023) – Austin Peay State University, Clarksville, TN
    • Advisor: Dr. Joseph Elarde
  • B.S. in Information Science (2013) – Abia State University, Uturu, Nigeria

Professional Experience

Ms. Oluomachi Ejiofor is a Graduate Teaching Fellow at the Department of Computer Science, Austin Peay State University, where she has been instructing and mentoring students since 2023. Previously, she worked as a Graduate Research Assistant (2021–2023) at the same institution, focusing on cybersecurity and AI-driven security models under the supervision of Dr. Joseph Elarde.

Before transitioning into academia, she served as a Cyber Analyst at Austin Peay State University, where she led a team in configuring IT infrastructure, implementing cybersecurity frameworks, and securing institutional data. Prior to that, she worked as a Data Administrator at Steps Tech Company, Nigeria (2018–2021), managing data security protocols, conducting security audits, and implementing encryption technologies to protect critical information.

Research Interest

  • Network Security & Cryptography
  • Malware Analysis & Cyber Threat Intelligence
  • Privacy & Anonymity in IoT
  • Cloud & Blockchain Security
  • AI & Machine Learning in Cybersecurity
  • Human Factors in Cybersecurity

Publications & Research Contributions

Ms. Ejiofor has authored several research papers in high-impact journals, covering areas such as:

  • Cybersecurity and AI-enhanced fraud detection
  • Privacy and data protection in wireless sensor networks
  • Cyber threat intelligence and risk mitigation
  • Blockchain and cloud security models

Her notable publications include:

  • Resilient Chain: AI-Enhanced Supply Chain Security and Efficiency Integration
  • Assessing the Effectiveness of Current Cybersecurity Regulations and Policies in the US
  • Enhancing Data Privacy in Wireless Sensor Networks for Healthcare & National Security
  • Cyber Analytics: Modeling the Factors Behind Healthcare Data Breaches
  • A Comprehensive Framework for Strengthening Financial Cybersecurity in the US

She is actively working on machine learning applications in financial risk assessment and AI-driven cybersecurity models for healthcare identity theft prevention.

Professional Affiliations & Certifications

Ms. Ejiofor is a Fellow of the Nigerian Institution of Professional Engineers and Scientists (NIPES) and the International Organization for Academic and Scientific Development (IOASD). She is also a member of the IEEE Computer Society, the Nigeria Computer Society (NCS), and the International Society of Data Science.

She holds certifications in Cybersecurity in Healthcare, Data Security & Privacy, Active Directory, and Microsoft Suite and has expertise in technical support, troubleshooting, and cybersecurity frameworks (NIST, CIA Triad).

Author Metrics

Ms. Ejiofor has a growing research impact in the cybersecurity domain, with multiple publications in peer-reviewed journals and conference proceedings. Her work on AI-enhanced cybersecurity models and fraud prevention frameworks has been cited in studies related to financial security, healthcare data protection, and cyber threat intelligence.

Top Noted Publication

Enhancing Cyber Financial Fraud Detection Using Deep Learning Techniques: A Study on Neural Networks and Anomaly Detection

  • Authors: OA Bello, A Folorunso, A Ogundipe, O Kazeem, A Budale, F Zainab, …
  • Journal: International Journal of Network and Communication Research, Vol. 7 (1), pp. 90-113, 2022
  • Citations: 104
  • Summary: This paper explores the application of deep learning models, particularly neural networks and anomaly detection, to improve fraud detection in financial transactions. The study demonstrates how advanced AI techniques can enhance the accuracy and efficiency of cybersecurity mechanisms in the financial sector.

Machine Learning Approaches for Enhancing Fraud Prevention in Financial Transactions

  • Authors: OA Bello, A Folorunso, OE Ejiofor, FZ Budale, K Adebayo, OA Babatunde
  • Journal: International Journal of Management Technology, Vol. 10 (1), pp. 85-108, 2023
  • Citations: 101
  • Summary: This study presents various machine learning strategies for detecting fraudulent activities in financial transactions. It evaluates the performance of supervised and unsupervised learning models in preventing cyber fraud.

A Comprehensive Framework for Strengthening USA Financial Cybersecurity: Integrating Machine Learning and AI in Fraud Detection Systems

  • Author: OE Ejiofor
  • Journal: European Journal of Computer Science and Information Technology, Vol. 11 (6), pp. 62-83, 2023
  • Citations: 101
  • Summary: This paper proposes a robust AI-driven framework for financial cybersecurity in the U.S., leveraging machine learning algorithms for fraud detection and mitigation. It provides policy recommendations for enhancing financial data security.

Analysing the Impact of Advanced Analytics on Fraud Detection: A Machine Learning Perspective

  • Authors: OA Bello, A Folorunso, J Onwuchekwa, OE Ejiofor, FZ Budale, …
  • Journal: European Journal of Computer Science and Information Technology, Vol. 11 (6), pp. 103-126, 2023
  • Citations: 100
  • Summary: This research examines how advanced analytics, including predictive modeling and deep learning, can improve fraud detection capabilities in financial institutions.

Mechanics and Computational Homogenization of Effective Material Properties of Functionally Graded (Composite) Material Plate FGM

  • Authors: C Mathew, O Ejiofor
  • Journal: International Journal of Scientific and Research Publications, Vol. 13 (9), pp. 128-150, 2023
  • Citations: 21
  • Summary: This paper explores the computational modeling of functionally graded materials (FGM), focusing on their mechanical properties and structural behavior.

Conclusion

Ms. Ejiofor is a strong contender for the Best Paper Award, given her highly cited research, AI-driven cybersecurity innovations, and practical contributions to fraud detection and data privacy. If she continues expanding her leadership in cybersecurity research, publishing in more high-impact journals, and emphasizing large-scale real-world applications, she will solidify her position as a top cybersecurity researcher.

Atif Rehman | AI & Cybersecurity | Best Researcher Award

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Mr. Atif Rehman | AI & Cybersecurity | Best Researcher Award

Vice President at NUST, Pakistan

Summary:

Mr. Atif Rehman is a graduate in Computational Sciences and Engineering with a special focus on control systems, having earned his Master’s degree from the National University of Science and Technology (NUST), Islamabad. He completed his undergraduate studies in Mathematics from the International Islamic University, Islamabad. His academic journey is marked by his commitment to integrating advanced mathematical techniques with engineering applications, particularly in the fields of nonlinear control systems and optimization. Mr. Rehman has contributed to the development of novel optimization algorithms aimed at improving control system performance, notably through his work on Grey Wolf Optimization for robust nonlinear controller design. He is passionate about using his knowledge to develop sustainable, efficient solutions in various domains, including healthcare and transportation.

Professional Profile:

👩‍🎓Education:

  • Master of Science in Computational Sciences and Engineering
    • Institution: National University of Science and Technology (NUST), Islamabad
    • Duration: September 2021 – August 2023
    • CGPA: 3.70 / 4.0
    • Principal Subjects: Linear Control Systems, Nonlinear Control Systems, Adaptive/Closed-loop Control, Sliding Mode Control, Advanced Machine Learning, Deep Learning
    • Research Thesis: “Improved Grey Wolf Optimization-Based Robust Nonlinear Controller Design for Prostate Cancer”
    • Focus: This research bridged the theoretical knowledge of control systems with their practical applications, specifically targeting health technology for prostate cancer treatments.
  • Bachelor of Science in Mathematics
    • Institution: International Islamic University (IIU), Islamabad
    • Duration: September 2017 – August 2021
    • CGPA: 3.61 / 4.0
    • Principal Subjects: Calculus, Linear Algebra, Real Analysis, Numerical Methods, Partial Differential Equations, Fluid Mechanics, Discrete Structures

🏢 Professional Experience:

Mr. Atif Rehman has a strong academic background that bridges both theoretical and applied mathematics, computational sciences, and engineering. His advanced studies in computational sciences and engineering with a focus on control systems, along with his extensive research in optimization techniques, provide him with the necessary skills to contribute significantly to the development of efficient systems. His research endeavors in robust nonlinear controller design and optimization algorithms demonstrate his capacity for both theoretical advancements and practical solutions.

His master’s thesis on designing a robust nonlinear controller for prostate cancer treatment using Grey Wolf Optimization reflects his interest in applying computational techniques to real-world problems. Additionally, his undergraduate studies in mathematics provided him with a robust understanding of the fundamental principles of calculus, linear algebra, and numerical methods, which laid the groundwork for his future research in control systems and optimization.

Research Interests:

Mr. Rehman’s research interests lie at the intersection of control systems, optimization techniques, and machine learning. Specifically, he is keen on the following areas:

  • Deep Reinforcement Learning: Applying reinforcement learning to optimize control systems.
  • Machine Learning: Using machine learning for system modeling and predictive control.
  • Adaptive Control Systems: Developing control strategies that adjust to changing system parameters over time, such as in biomedical applications where system parameters may vary across individuals or conditions.
  • Optimization Techniques: Implementing advanced optimization algorithms like Improved Grey Wolf Optimization, Genetic Algorithms, and reinforcement learning-based optimization to solve complex control problems.

Author Metrics:

  • Publications:
    1. Improved Grey Wolf Optimization-Based Robust Nonlinear Controller Design for Prostate Cancer
    2. Optimized Nonlinear Robust Controller Along with Model-Parameter Estimation for Blood Glucose Regulation in Type-1 Diabetes
    • His works primarily focus on optimization techniques, machine learning, and adaptive control, showing substantial contributions to both academia and practical applications.
  • Citations: His research in robust controller design and adaptive systems has garnered attention in related academic circles, contributing to advancements in both theoretical studies and practical solutions for control systems in biomedical applications.

Top Noted Publication:

Artificial Intelligence-Based Robust Nonlinear Controllers Optimized by Improved Grey Wolf Optimization Algorithm for Plug-In Hybrid Electric Vehicles in Grid-to-Vehicle Applications

  • Authors: S. Saleem, I. Ahmad, S.H. Ahmed, A. Rehman
  • Journal: Journal of Energy Storage
  • Publication Year: 2024
  • Citations: 15
  • Summary: This study presents an AI-driven, robust nonlinear controller design optimized using an Improved Grey Wolf Optimization (IGWO) algorithm for enhancing the energy management and performance of plug-in hybrid electric vehicles (PHEVs) in grid-to-vehicle systems.

Advancing Optimized Nonlinear Control Strategies for Cancerous Tumor Dynamics

  • Authors: A. Rehman, R. Ghias, S.H.A. Shah, S. Saleem, I. Ahmad
  • Conference: 2023 2nd International Conference on Emerging Trends in Electrical, Control, and Instrumentation Engineering
  • Publication Year: 2023
  • Citations: 3
  • Summary: This paper explores the development of optimized nonlinear control strategies tailored to manage and mitigate the complex dynamics of tumor growth in cancer patients.

A Novel Approach to Nonlinear Control in Tuberculosis Transmission Dynamics

  • Authors: S.H. Ahmed, A. Rehman, I. Ahmad
  • Conference: 2023 2nd International Conference on Emerging Trends in Electrical, Control, and Instrumentation Engineering
  • Publication Year: 2023
  • Citations: 3
  • Summary: This research presents a unique methodology for applying nonlinear control theory to model and manage tuberculosis transmission, offering insights into effective intervention strategies.

Advance Optimized Nonlinear Control Strategies for Managed Pressure Drilling

  • Authors: A. Rehman, R. Ghias, I. Ahmad, H.I. Sherazi
  • Journal: IEEE Access
  • Publication Year: 2024
  • Citations: 2
  • Summary: The study introduces enhanced nonlinear control techniques optimized using IGWO for managing pressure during drilling operations, which is crucial for operational safety and efficiency in the oil and gas industry.

IGWO-Based Robust Nonlinear Control Design for Androgen Suppression Therapy in Prostate Tumor Patients

  • Authors: A. Rehman, I. Ahmad, A.U. Jabbar
  • Journal: Biomedical Signal Processing and Control
  • Publication Year: 2024
  • Summary: This paper outlines the design of a robust nonlinear control system optimized by IGWO for regulating androgen suppression therapy in prostate cancer treatment, showcasing significant improvements in treatment strategies through adaptive control techniques.

Conclusion:

Mr. Atif Rehman’s robust academic foundation, innovative research contributions in AI, cybersecurity, and optimization techniques, and dedication to applying his knowledge to practical problems make him a strong candidate for the Best Researcher Award. By addressing areas for improvement, such as expanding his publication record and participating in international collaborations, he could further solidify his reputation as an influential researcher. His trajectory suggests a promising future marked by continued advancements and interdisciplinary contributions.