Mistura L. Sanni

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

E-mail: msanni@oauife.edu.ng

Website: https://orcid.org/0000-0001-9206-4009

Research Interests: Computational Science and Engineering, Interaction Design, Data Structures and Algorithms, Algorithm Design

Biography

Mistura L. Sanni obtained her BSc, M.Sc. (Computer Engineering) in 1992 and 2006 respectively from Obafemi Awolowo University, Ile-Ife. from and Ph.D.  (Computer Engineering) from the International Islamic University, Malaysia in 2015 and currently a Senior Lecturer in the Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife. She specializes in hardware design and data communication Networks. She is a certified COREN, NCS, CPN member.

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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