Joy Okolo Unveils New Pathways for Cybersecurity in the Internet of Devices

By Tosin Clegg

As more connected devices flood homes, hospitals, and industries across the globe, the threat of cyberattacks and privacy breaches continues to rise.

Renowned computer scientist and business analyst Joy Nnenna Okolo has called for stronger integration of artificial-intelligence tools, particularly machine-learning (ML) and deep-learning (DL) models, to safeguard the Internet of Devices (IoD) from growing digital threats.

She shared this in a detailed paper titled “A Review of Machine and Deep Learning Approaches for Enhancing Cybersecurity and Privacy in the Internet of Devices,” a research review that critically examines how ML and DL can be applied to detect, prevent, and mitigate cyber risks in the increasingly interconnected world of smart devices.

In her study, Okolo explains that as systems become more autonomous, spanning smart cities, driverless cars, e-health systems, and industrial networks, the volume of data transmitted without human oversight has reached unprecedented levels. This data, often containing sensitive personal or operational information, has become a lucrative target for cybercriminals seeking to exploit vulnerabilities in IoD systems.

According to Okolo, ML and DL models have emerged as powerful tools for defending such environments. These systems can learn from data patterns to detect anomalies, recognize potential breaches in real time, and even predict future attack trends. “Data itself plays a dominant role,” she notes, “because ML/DL models depend heavily on quality datasets to track traffic patterns and identify abnormalities.”

Her paper goes beyond theoretical discussion, identifying key datasets used for network-traffic monitoring and intrusion detection. These datasets, she explains, form the backbone of cyber-defence systems, enabling algorithms to distinguish between normal and malicious activity. Yet, she cautions that data collection and labeling remain among the most persistent challenges in cybersecurity research.

Okolo highlights several limitations of current ML and DL methods, including model overfitting, scalability constraints, and the lack of interpretability in deep neural networks. She stresses the need for hybridized approaches that combine the strengths of both ML and DL while reducing computational cost.

The study further explores privacy-preserving techniques for IoD environments. With billions of interconnected devices transmitting user data from wearable sensors to smart grids, Okolo advocates the adoption of federated-learning and differential-privacy models, which allow data to be processed locally on devices without compromising individual privacy.

Her review also outlines potential future directions for cybersecurity innovation, including the creation of self-adaptive security frameworks capable of learning from evolving attack vectors. She believes that by embedding intelligence into IoD architecture, systems can autonomously respond to threats and adjust defense mechanisms dynamically.

Okolo has consistently demonstrated the ability to transform complex datasets into actionable insights that drive strategic outcomes. She has designed and implemented automation frameworks that reduced reporting time by nearly half and improved data accuracy across multiple business systems.
Throughout her career, Okolo has led analytics initiatives focused on operational efficiency, customer engagement, and performance optimization. By integrating predictive models and interactive dashboards, she has enabled data-driven decision-making that improved visibility and efficiency across organizational processes.

Her earlier experience at Access Bank Plc in financial data analytics underscored her deep understanding of how data can inform strategic growth. Okolo developed and executed customer-retention programs that reactivated dormant accounts, expanded user bases, and boosted satisfaction metrics by double-digit margins. Her analytical rigor and forward-thinking mindset established her as a trusted innovator in the evolving field of data-driven systems.

With a multidisciplinary foundation in computer science, data analytics, and systems optimisation, Joy Okolo continues to advance the frontier of technology and cybersecurity, pioneering practical and intelligent solutions that make the digital ecosystem more secure, adaptive, and resilient.