Malicious Files Detection using Machine Learning |
Author(s): |
| Nisha Chaudhary , B.K Birla College of Arts, Science and Commerce (Autonomous) |
Keywords: |
| Malicious Files, Machine Learning |
Abstract |
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In a comprehensive study on malware detection, we evaluated five machine learning models: multinomial naive bayes, random forest classifiers, K-nearest neighbours, decision tree classifiers, and support vector classifiers, using a diverse dataset with file feature extraction. Results showcased varying performances, with the Random Forest Classifier standing out with a flawless accuracy score of 1.00, excelling in distinguishing between malware and legitimate files. The Decision Tree Classifier and K-Nearest Neighbours Classifier followed closely, both achieving a high accuracy of 0.99. This research underscores the effectiveness of machine learning models, especially Random Forest, in enhancing cybersecurity by accurately identifying potential threats amidst digital files. |
Other Details |
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Paper ID: IJSRDV11I80044 Published in: Volume : 11, Issue : 8 Publication Date: 01/11/2023 Page(s): 76-78 |
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