Presentation Details
Accurate Malware Detection Using Random Forest and XGBoost with Cross-Validation and ROC-AUC Analysis
ID：119
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Updated Time：2026-07-22 16:10:09 Hits：33
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Start Time：2026-07-30 15:55（Asia/Kolkata）
Duration：15min
Session：S3 Cyber Security »
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Abstract
Malware detection plays a vital role in cybersecurity, aiming to accurately identify malicious software while minimizing false positives. However, existing approaches often struggle with generalization, especially when models are trained on large but imbalanced datasets, highlighting a research gap. This study proposes a machine learning-based malware detection framework using a CSV-based static feature dataset containing system-level and behavioral attributes extracted from executable files. The dataset consists of 100,000 balanced samples of benign and malicious files, partitioned into distinct training and testing sets. Two classification algorithms, Random Forest and XGBoost, were employed to classify files as either benign or malicious. Model performance was evaluated using stratified five-fold cross-validation and multiple metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, with confusion matrices, ROC curves, and precision–recall curves examined for further insight. The results achieved perfect scores across all metrics (100% accuracy, precision, recall, F1-score, and ROC-AUC), indicating that the models likely overfitted the data rather than learning to generalize. These findings emphasize the need for more careful data partitioning and the incorporation of data augmentation in future work. The proposed framework provides a foundation for developing more reliable malware detection systems in real-world cybersecurity applications.
Keywords
Malware Detection,Machine Learning,Random Forest,XGBoost;,Cybersecurity.
Speaker
Basel Dabwan
PHD STUDENT ALBAHA PRIVATE COLLEGE OF SCIENCESubmission Author
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Important Date
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Conference Date
Jul 30
2026
toAug 01
2026
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Jul 28 2026
Draft paper submission deadline
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Aug 03 2026
Registration deadline
Sponsored By
The United Societies of Science
Organized By
Kongunadu College of Engineering and Technology
Supported By
IEEE Section
IEEE Madras Section
IEEE Madras Section
Contact Information
Previous Conferences
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2026-07-30 India Trichy
2026 International Conference on Networks Computers and Communications
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