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Accurate Malware Detection Using Random Forest and XGBoost with Cross-Validation and ROC-AUC Analysis
编号：119 访问权限：仅限参会人 更新：2026-07-22 16:10:09 浏览：34次 Online

报告开始：2026年07月30日 15:55（Asia/Kolkata）

报告时间：15min

所在会场：[S3] Cyber Security [S3-2] Cyber Security

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摘要
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.
 
关键词
Malware Detection,Machine Learning,Random Forest,XGBoost;,Cybersecurity.
报告人
Basel Dabwan
PHD STUDENT ALBAHA PRIVATE COLLEGE OF SCIENCE

稿件作者
Basel Dabwan ALBAHA PRIVATE COLLEGE OF SCIENCE
Yusif Elfatih ALBAHA PRIVATE COLLEGE OF SCIENCE
Somia Badawi Najran University
Nabila Saeed Saeed Najran University
Majda Elbasheer Najran University
Arwa Eldhai Najran University
YAHYA ALI Najran University
Ioannou Iacovos University of Cyprus
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重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

  • 07月28日 2026

    初稿截稿日期

  • 08月03日 2026

    注册截止日期

主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
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