Ms. Oluomachi Ejiofor | Cybersecurity | Best Paper Award

Cybersecurity Analyst at Austin peay state Unversity, 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