부산대학교 도서관

In this research paper, we'll be diving into the combination of malware analysis and machine learning to stepup security. Right now, our digital world is like an anthill: connected in every possible way. In this study, we used machine learning algorithms like Random Forests, K-Nearest Neighbours (KNN), and Logistic Regression for anomaly detection. Inside 60,000 benign and malware instances lie the answers we are looking for. We judge these three algorithms on how well they can detect accuracy, precision, recall, and Fl- score. We hope to give professionals practical insights to secure their systems from malware while constantly being aware of new threats. In a day where security is crucial, this work will connect past issues with machine learning today so that we can better prepare for problems tomorrow.