L. Huraj

Random Forest-Based Network Intrusion Detection with Feature Selection and Class Balancing on UNSW-NB15 Traffic

Machine-learning intrusion detection is challenged by attacks resembling legitimate traffic and by class imbalance. This study evaluates Random Forest detection on the UNSW-NB15 dataset for five binary attack-versus-normal tasks: DoS, Exploit, Backdoor, Analysis, and Reconnaissance. Categorical attributes were label-en...