An adaptive neuro-fuzzy inference system for multinomial malware classification

Authors

  • Amos Orenyi Bajeh
    Department of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria
  • Mary Olayinka Olaoye
    Department of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria
  • Fatima Enehezei Usman-Hamza
    Department of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria
  • Ikeola Suhurat Olatinwo
    Department of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria
  • Peter ogirima Sadiku
    Department of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria
  • Abdulkadir Bolakale Sakariyah
    Department of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria

Keywords:

Malware, Adaptive neuro-fuzzy inference system, Artificial intelligence, Fuzzy logic, Artificial neural network

Abstract

Malware detection and classification are important requirements for information security because malware poses a great threat to computer users. As the growth of technology increases, malware is getting more sophisticated and thereby more difficult to detect. Machine learning techniques have been extensively used for malware detection and classification. However, most of them are binomial classifications that only detect the presence of malware but do not classify them into types.  This study sets out to develop a multinomial malware classifier using an adaptive neuro-fuzzy inference system (ANFIS) and investigate the effectiveness of ANFIS in the classification. A first-order Sugeno ANFIS model was developed. It has five layers and uses two if-then rules. The ANFIS model was trained and tested with two prominent malware datasets from the Canada Institute of Cyber Security. The experimental results showed that the performance of the ANFIS model degrades as the size of the datasets increases, and the accuracy, precision, recall, and root mean square error is 94%, 0.88, 0.87, and 0.19 respectively.

Dimensions

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Published

2025-02-01

How to Cite

An adaptive neuro-fuzzy inference system for multinomial malware classification. (2025). Journal of the Nigerian Society of Physical Sciences, 7(1), 2172. https://doi.org/10.46481/jnsps.2025.2172

Issue

Volume 7, Issue 1, February 2025

Section

Computer Science
Copyright & Licensing

Copyright (c) 2024 Amos Orenyi Bajeh, Mary Olayinka Olaoye, Fatima Enehezei Usman-Hamza, Ikeola Suhurat Olatinwo, Peter ogirima Sadiku, Abdulkadir Bolakale Sakariyah

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

An adaptive neuro-fuzzy inference system for multinomial malware classification. (2025). Journal of the Nigerian Society of Physical Sciences, 7(1), 2172. https://doi.org/10.46481/jnsps.2025.2172

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