Paper Title

The spread of malware in today's digital environment is a serious problem that affects people, organizations, and communities everywhere. Our paper thoroughly examines machine learning (ML) algorithms and their role in accurately detecting malware. In this study, numerous machine learning models, including XGBoost, Logistic Regression, Support Vector Machine, K-Nearest Neighbors Classifier, AdaBoost, Random Forest, and SDG Classifier, are used to evaluate a labeled dataset consisting of malware and benign file samples. Data preprocessing, training, testing, and feature extraction are the steps taken in this process. After that, metrics for accuracy and execution times are used to conduct a comparison study. In terms of accuracy rates and reliable performance metrics, the results show that ensemble methods—Random Forest and XGBoost in particular—regularly beat other algorithms in accurately predicting a data point to be malware or benign sample. Furthermore, our study also highlights the importance of confusion matrices in evaluating the efficiency of classification models and identifies XGBOOST as a potentially useful technique for malware detection and classification. Future study is to investigate deep learning techniques, transfer learning, and real-time malware detection to enhance detection accuracy and reaction capabilities for malware. Keywords - Malware, Machine Learning, Classification, Random Forest, Confusion Matrix