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Edosa Osa

Work place: Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Benin, P.M.B. 1154, Benin City, Nigeria

E-mail: edosa.osa@uniben.edu

Website:

Research Interests:

Biography

Edosa Osa obtained M. Eng in Electronics and Telecommunications and B. Eng in Electrical and Electronics Engineering from the University of Benin, Nigeria in 2013 and 2008 respectively. He also obtained a Post Graduate Diploma in Digital Forensics with Distinction from the University of Benin, Nigeria in the year 2024. 
He is currently a Lecturer at the University of Benin, Benin City, Nigeria. His areas of research interest include Computer Networking, Control systems, cybersecurity, digital forensics and artificial intelligence.
Mr. Osa also possesses the following:
Membership in Professional Societies
Council for the Regulation of Engineering in Nigeria.
Nigerian Society of Engineers.
Computer Forensics Institute of Nigeria (CFIN).
IEEE Society.
Certifications 
Cisco Network Associate. 
AWS Cloud Practitioner.
Some Publications
Konyeha, S. and Osa, E. (2020). Deployment of VANETs Infrastructure to Aid Road Transport Systems in Developing 
Countries, EJECE, European Journal of Electrical Engineering and Computer Science Vol. 4, No. 6, November 2020. DOI: https://doi.org/10.24018/ejece.2020.4.6.253.
E. Osa, E. Evbuomwan and E. E. Ajari, "Design of an Autonomous Underwater Vehicle EDYSYS 1," 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), 2021, pp. 1-4, ©2021 IEEE | DOI: 10.1109/ICMEAS52683.2021.9692421.
Osa, E. and Ikponwomba, E.A. (2021). Deployment of Machine Learning Models in Cybersecurity: A Review. The 1st International Conference of the Nigerian Institution of Professional Engineers and Scientists 7th-8th Oct. 2021: NIPES Conference Proceedings pp.350-359.
E. Osa and O. E. Oghenevbaire, "Comparative Analysis of Machine Learning Models in Computer Network Intrusion Detection," 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), 2022, pp. 1-5, DOI: 10.1109/NIGERCON54645.2022.9803175.
Edosa Osa, Patience E. Orukpe, Usiholo Iruansi, Design and implementation of a deep neural network approach for intrusion detection systems, e-Prime - Advances in Electrical Engineering, Electronics and Energy, Volume 7, 2024, 100434, ISSN 2772-6711, https://doi.org/10.1016/j.prime.2024.100434. (https://www.sciencedirect.com/science/article/pii/S2772671124000160).

Author Articles
Performance Analysis of Shallow and Deep Learning Classifiers Leveraging the CICIDS 2017 Dataset

By Edosa Osa Emmanuel J. Edifon Solomon Igori

DOI: https://doi.org/10.5815/ijisa.2025.02.04, Pub. Date: 8 Apr. 2025

In order to implement the advantages of machine learning in the cybersecurity ecosystem, various anomaly detection-based models are being developed owing to their ability to flag zero-day attacks over their signature-based counterparts. The development of these anomaly detection-based models depends heavily on the dataset being employed in terms of factors such as wide attack pool or diversity. The CICIDS 2017 stands out as a relevant dataset in this regard. This work involves an analytical comparison of the performances by selected shallow machine learning algorithms as well as a deep learning algorithm leveraging the CICIDS 2017 dataset. The dataset was imported, pre-processed and necessary feature selection and engineering carried out for the shallow learning and deep learning scenarios respectively. Outcomes from the study show that the deep learning model presented the highest performance of all with respect to accuracy score, having percentage value as high as 99.71% but took the longest time to process with 550 seconds. Furthermore, some shallow learning classifiers such as Decision Tree and Random Forest took less processing time (4.567 and 3.95 seconds respectively) but had slightly less accuracy scores than the deep learning model with the CICIDS 2017 dataset. Results from our study show that Deep Neural Network is a viable model for intrusion detection with the CICIDS 2017 dataset. Furthermore, the results of this study are to provide information that may influence choices while developing machine learning based intrusion detection systems with the CICIDS 2017 dataset.

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