An adaptive neuro-fuzzy inference system for multinomial malware classification
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 e...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nigerian Society of Physical Sciences
2025-02-01
|
| Series: | Journal of Nigerian Society of Physical Sciences |
| Subjects: | |
| Online Access: | https://journal.nsps.org.ng/index.php/jnsps/article/view/2172 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841525254279659520 |
|---|---|
| author | Amos Orenyi Bajeh Mary Olayinka Olaoye Fatima Enehezei Usman-Hamza Ikeola Suhurat Olatinwo Peter ogirima Sadiku Abdulkadir Bolakale Sakariyah |
| author_facet | Amos Orenyi Bajeh Mary Olayinka Olaoye Fatima Enehezei Usman-Hamza Ikeola Suhurat Olatinwo Peter ogirima Sadiku Abdulkadir Bolakale Sakariyah |
| author_sort | Amos Orenyi Bajeh |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-c9b8defead3f4a878f5aaad1a69292ed |
| institution | Kabale University |
| issn | 2714-2817 2714-4704 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | Journal of Nigerian Society of Physical Sciences |
| spelling | doaj-art-c9b8defead3f4a878f5aaad1a69292ed2025-01-17T18:52:28ZengNigerian Society of Physical SciencesJournal of Nigerian Society of Physical Sciences2714-28172714-47042025-02-017110.46481/jnsps.2025.2172An adaptive neuro-fuzzy inference system for multinomial malware classificationAmos Orenyi Bajeh0Mary Olayinka Olaoye1Fatima Enehezei Usman-Hamza2Ikeola Suhurat Olatinwo3Peter ogirima Sadiku4Abdulkadir Bolakale Sakariyah5Department of Computer Science, University of Ilorin, Ilorin, 240003, NigeriaDepartment of Computer Science, University of Ilorin, Ilorin, 240003, NigeriaDepartment of Computer Science, University of Ilorin, Ilorin, 240003, NigeriaDepartment of Computer Science, University of Ilorin, Ilorin, 240003, NigeriaDepartment of Computer Science, University of Ilorin, Ilorin, 240003, NigeriaDepartment of Computer Science, University of Ilorin, Ilorin, 240003, Nigeria 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. https://journal.nsps.org.ng/index.php/jnsps/article/view/2172MalwareAdaptive neuro-fuzzy inference systemArtificial intelligenceFuzzy logicArtificial neural network |
| spellingShingle | Amos Orenyi Bajeh Mary Olayinka Olaoye Fatima Enehezei Usman-Hamza Ikeola Suhurat Olatinwo Peter ogirima Sadiku Abdulkadir Bolakale Sakariyah An adaptive neuro-fuzzy inference system for multinomial malware classification Journal of Nigerian Society of Physical Sciences Malware Adaptive neuro-fuzzy inference system Artificial intelligence Fuzzy logic Artificial neural network |
| title | An adaptive neuro-fuzzy inference system for multinomial malware classification |
| title_full | An adaptive neuro-fuzzy inference system for multinomial malware classification |
| title_fullStr | An adaptive neuro-fuzzy inference system for multinomial malware classification |
| title_full_unstemmed | An adaptive neuro-fuzzy inference system for multinomial malware classification |
| title_short | An adaptive neuro-fuzzy inference system for multinomial malware classification |
| title_sort | adaptive neuro fuzzy inference system for multinomial malware classification |
| topic | Malware Adaptive neuro-fuzzy inference system Artificial intelligence Fuzzy logic Artificial neural network |
| url | https://journal.nsps.org.ng/index.php/jnsps/article/view/2172 |
| work_keys_str_mv | AT amosorenyibajeh anadaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT maryolayinkaolaoye anadaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT fatimaenehezeiusmanhamza anadaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT ikeolasuhuratolatinwo anadaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT peterogirimasadiku anadaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT abdulkadirbolakalesakariyah anadaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT amosorenyibajeh adaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT maryolayinkaolaoye adaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT fatimaenehezeiusmanhamza adaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT ikeolasuhuratolatinwo adaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT peterogirimasadiku adaptiveneurofuzzyinferencesystemformultinomialmalwareclassification AT abdulkadirbolakalesakariyah adaptiveneurofuzzyinferencesystemformultinomialmalwareclassification |