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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
ijacsa
2023
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10146/1/J16272_30a298c35bf60d5e04107f3a4fda2495.pdf http://eprints.uthm.edu.my/10146/ https://doi.org/10.13140/RG.2.2.11478.16963. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | —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. |
|---|