By Ayo Onikoyi

Osawere who has continued to revolutionized the field of cybersecurity with her pioneering research on ransomware detection, explored novel approaches to identifying ransomware families by analyzing network traffic using advanced machine learning techniques.

Her work marks a significant breakthrough in the ongoing battle against cyber threats, offering new insights and strategies for safeguarding sensitive information in an increasingly digital world.

With a robust educational background culminating in a Master of Science Degree in Computer Science from North Carolina Agricultural and Technical State University in 2021, coupled with a specialization in cybersecurity, Osawere possesses a comprehensive understanding of the intricate intersection between technology and business imperatives.

Her professional journey has been marked by significant contributions, notably during her tenure as a Software Developer at Oracle Corporation.

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She played a pivotal role in crafting highly scalable cloud security tools within the Oracle Cloud Infrastructure division, ensuring optimal performance and reliability of secure software solutions.

Osawere’s passion for fortifying defensive measures and safeguarding sensitive information extends beyond practical applications to groundbreaking research initiatives.

Her notable publication, “Identification of Ransomware Families by Analyzing Network Traffic Using Machine Learning Techniques,” delves into innovative methodologies for ransomware detection through the analysis of network traffic employing machine learning algorithms.

In this research, Osawere and her colleagues developed multi-class classification models to detect ransomware families by leveraging selected network traffic features, particularly focusing on Transmission Control Protocol (TCP) traffic features.

Their experiments showcased the remarkable accuracy of decision trees in classifying ransomware families, slightly outperforming other algorithms with a 99.83% accuracy rate.

Furthermore, their research identified ten crucial features for ransomware detection, including time delta, frame length, IP length, IP destination, IP source, TCP length, TCP sequence, TCP next sequence, TCP header length, and TCP initial round trip.

This pioneering work has earned recognition and acceptance for publication in the prestigious Institute of Electrical and Electronics Engineers (IEEE) journal, underscoring its significance in the cybersecurity landscape.

Osawere’s contributions were also showcased at the esteemed 2021 Third International Conference on Transdisciplinary AI, where her research was part of the conference proceedings.

This recognition further solidifies her position as a trailblazer in cybersecurity research.

Beyond her technical proficiency, Osawere’s skill set encompasses critical analysis, strategic decision-making, and effective communication, highlighting her unwavering dedication to excellence and innovation in technology-driven environments.