Bodunde O. Akinyemi

Work place: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

E-mail: bakinyemi@oauife.edu.ng

Website: https://orcid.org/0000-0001-6943-7137

Research Interests: Information Security, Network Architecture, Network Security, Database Management System, Data Structures and Algorithms

Biography

Bodunde O. Akinyemi holds B.Tech (2005) in Computer Science from Ladoke Akintola University Ogbomosho, M.Sc. (2011) and Ph.D. (2014) in Computer Science from Obafemi Awolowo University Ile-Ife.  She is a member of the International Association Engineers, IEEE, Nigeria Computer Society (NCS) and Computer Professional Registration Council of Nigeria (CPN). She is a Senior Lecturer and member of the Data Communication Group in the Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife. Her major research areas are in Data Communication, Network Security and Performance management, Software Development and BlockChain Technology.

Author Articles
Performance Evaluation of Machine Learning-based Robocalls Detection Models in Telephony Networks

By Bodunde O. Akinyemi Oluwatoyin H. Odukoya Mistura L. Sanni Gilbert Sewagnon Ganiyu A. Aderounmu

DOI: https://doi.org/10.5815/ijcnis.2022.06.04, Pub. Date: 8 Dec. 2022

Many techniques have been proposed to detect and prevent spam over Internet telephony. Human spam calls can be detected more accurately with these techniques. However, robocalls, a type of voice spammer whose calling patterns are similar to those of legitimate users, cannot be detected as effectively. This paper proposes a model for robocall detection using a machine learning approach. Voice data recordings were collected and the relevant features for study were selected. The selected features were then used to formulate six (6) detection models. The formulated models were simulated and evaluated using some performance metrics to ascertain the model with the best performance. The C4.5 decision tree algorithm gave the best evaluation result with an accuracy of 99.15%, a sensitivity of 0.991%, a false alarm rate of 0.009%, and a precision of 0.992%. As a result, it was concluded that this approach can be used to detect and filter both machine-initiated and human-initiated spam calls.

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