Detecting Malware with Classification Machine Learning Techniques

In today's digital landscape, the identification of malicious software has become a crucial undertaking. The ever-growing volume malware threats renders conventional signature-based methods insufficient in shielding against novel and intricate attacks. Consequently, machine learning strategies have surfaced as viable means detecting malware. following research report focuses on implementation classification for study assesses effectiveness several algorithms, including Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest, Logistic Regression, through an examination publicly accessible dataset featuring both benign files Additionally, influence diverse feature sets preprocessing techniques classifiers' performance is explored. outcomes investigation exhibit that can capably identify malware, attaining elevated precision levels decreasing false positive rates. Tree Forest display superior compared to other algorithms with 100.00% accuracy. Furthermore, it observed selection dimensionality reduction notably enhance classifier while mitigating computational complexity. Overall, this underscores potential approaches offers valuable guidance development successful detection systems.