Malware Detection Using Machine Learning

Malware is a major cybersecurity threat because malicious files and programs can steal information, damage systems,disrupt services, and provide unauthorized access. Traditional signature-based detection methods are useful for knownthreats but may struggle to identify previously unseen or modified malware. This paper presents a machine-learning-basedapproach for classifying files or executable samples as benign or malicious using extracted features. The proposedworkflow includes dataset collection, data cleaning, feature preparation, model training, testing, and performanceevaluation. Candidate classifiers may include Decision Tree, Random Forest, Support Vector Machine, or other suitablemodels; the final model should be selected using measured results on a held-out test set. Evaluation should report metricssuch as precision, recall, F1-score, accuracy, and confusion matrix. The approach is intended to support automated malwarescreening, while recognizing that model performance depends on dataset quality, feature selection, and the ability ofmalware to change over time. All experimental values in this draft are placeholders and must be replaced with results fromthe student's actual implementation.