Ganiyu A. Aderounmu

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

E-mail: gaderoun@oauife.edu.ng

Website: https://orcid.org/0000-0002-7992-514X

Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computer Architecture and Organization, Computer Networks, Data Structures and Algorithms

Biography

Ganiyu A. Aderounmu is a professor of Computer Science and Engineering from Obafemi Awolowo University, Ile-Ife, Nigeria. He is a Full member of the Nigeria Society of Engineers (NSE) and also a registered Computer Engineer with Council for Regulation of Engineering Practice in Nigeria (COREN). He is also a Full member of Nigeria Computer Society (NCS) and Computer Professional Registration Council of Nigeria (CPN). He has over 30years of experience in teaching and research. He is an author of many journal articles in Nigeria and abroad. His special interest includes computer communication and network. He is a visiting Research Fellow to the University of Zululand, Republic of South Africa. He was the former head of the Department of computer Science & Engineering, former Dean, Faculty of Technology, former President of Nigeria Computer Society (NCS), and the former Director of Information Technology and Communication Unit (INTECU).

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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