Enhancing CyberSecurity Through Random Forests: A Comprehensive Analysis of Malware Detection and Intrusion Detection
R. K. Satyardha Reddy, K. Saikumar, E Siva Nageswara Rao, P. Rajarajeswari · 2023
This study explores the application of Random Forests, a machine learning algorithm, in the field of cybersecurity. Specifically, it investigates the effectiveness of Random Forests in malware detection and intrusion detection. Through experiments conducted on relevant datasets, the study demonstrates the robust performance of Random Forests in accurately classifying malware samples and detecting various types of network attacks. The interpretability of Random Forests also provides valuable insights for security analysts to understand the indicators and behavioral patterns of malware and attacks. The findings highlight the potential of Random Forests as a practical and effective tool for enhancing cybersecurity defenses.