Detecting Malware with Classification Machine Learning Techniques
In today's digital landscape, the identification of malicious software has become a crucial undertaking. The evergrowing volume of malware threats renders conventional signature-based methods insufficient in shielding against novel and intricate attacks. Consequently, machine learning strategi...
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| Format: | Article |
| Language: | en |
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ijacsa
2023
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| Online Access: | http://eprints.uthm.edu.my/10545/1/J16272_30a298c35bf60d5e04107f3a4fda2495.pdf http://eprints.uthm.edu.my/10545/ |
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| author | Mohd Yusof, Mohd Azahari Abdullah, Zubaile Hamid Ali, Firkhan Ali Mohamad Sukri, Khairul Amin Shaker Hussain, Hanizan |
| author_facet | Mohd Yusof, Mohd Azahari Abdullah, Zubaile Hamid Ali, Firkhan Ali Mohamad Sukri, Khairul Amin Shaker Hussain, Hanizan |
| author_sort | Mohd Yusof, Mohd Azahari |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In today's digital landscape, the identification of malicious software has become a crucial undertaking. The evergrowing volume of malware threats renders conventional signature-based methods insufficient in shielding against novel
and intricate attacks. Consequently, machine learning strategies have surfaced as a viable means of detecting malware. The following research report focuses on the implementation of classification machine learning methods for detecting malware. The study assesses the effectiveness of several algorithms, including Naïve Bayes, Support Vector Machine (SVM), KNearest Neighbor (KNN), Decision Tree, Random Forest, and Logistic Regression, through an examination of a publicly accessible dataset featuring both benign files and malware. Additionally, the influence of diverse feature sets and preprocessing techniques on the classifiers' performance is explored. The outcomes of the investigation exhibit that machine learning methods can capably identify malware, attaining elevated precision levels and decreasing false positive rates. Decision Tree and Random Forest display superior performance compared to other algorithms with 100.00% accuracy. Furthermore, it is observed that feature selection and dimensionality reduction techniques can notably enhance classifier effectiveness while mitigating computational complexity. Overall, this research underscores the potential of machine learning approaches for detecting malware and offers valuable guidance for the development of successful malware detection systems. |
| format | Article |
| id | my.uthm.eprints-10545 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | ijacsa |
| record_format | eprints |
| spelling | my.uthm.eprints-105452024-01-03T01:36:05Z http://eprints.uthm.edu.my/10545/ Detecting Malware with Classification Machine Learning Techniques Mohd Yusof, Mohd Azahari Abdullah, Zubaile Hamid Ali, Firkhan Ali Mohamad Sukri, Khairul Amin Shaker Hussain, Hanizan T Technology (General) In today's digital landscape, the identification of malicious software has become a crucial undertaking. The evergrowing volume of malware threats renders conventional signature-based methods insufficient in shielding against novel and intricate attacks. Consequently, machine learning strategies have surfaced as a viable means of detecting malware. The following research report focuses on the implementation of classification machine learning methods for detecting malware. The study assesses the effectiveness of several algorithms, including Naïve Bayes, Support Vector Machine (SVM), KNearest Neighbor (KNN), Decision Tree, Random Forest, and Logistic Regression, through an examination of a publicly accessible dataset featuring both benign files and malware. Additionally, the influence of diverse feature sets and preprocessing techniques on the classifiers' performance is explored. The outcomes of the investigation exhibit that machine learning methods can capably identify malware, attaining elevated precision levels and decreasing false positive rates. Decision Tree and Random Forest display superior performance compared to other algorithms with 100.00% accuracy. Furthermore, it is observed that feature selection and dimensionality reduction techniques can notably enhance classifier effectiveness while mitigating computational complexity. Overall, this research underscores the potential of machine learning approaches for detecting malware and offers valuable guidance for the development of successful malware detection systems. ijacsa 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10545/1/J16272_30a298c35bf60d5e04107f3a4fda2495.pdf Mohd Yusof, Mohd Azahari and Abdullah, Zubaile and Hamid Ali, Firkhan Ali and Mohamad Sukri, Khairul Amin and Shaker Hussain, Hanizan (2023) Detecting Malware with Classification Machine Learning Techniques. International Journal of Advanced Computer Science and Applications,, 14 (6). pp. 167-172. |
| spellingShingle | T Technology (General) Mohd Yusof, Mohd Azahari Abdullah, Zubaile Hamid Ali, Firkhan Ali Mohamad Sukri, Khairul Amin Shaker Hussain, Hanizan Detecting Malware with Classification Machine Learning Techniques |
| title | Detecting Malware with Classification Machine Learning Techniques |
| title_full | Detecting Malware with Classification Machine Learning Techniques |
| title_fullStr | Detecting Malware with Classification Machine Learning Techniques |
| title_full_unstemmed | Detecting Malware with Classification Machine Learning Techniques |
| title_short | Detecting Malware with Classification Machine Learning Techniques |
| title_sort | detecting malware with classification machine learning techniques |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10545/1/J16272_30a298c35bf60d5e04107f3a4fda2495.pdf http://eprints.uthm.edu.my/10545/ |
| url_provider | http://eprints.uthm.edu.my/ |