Detecting Neptune Attacks with Random Forest

The Neptune Attack Prediction Model utilizes a Random Forest classifier to detect Neptune attacks, a type of denial-of-service (DoS) attack overwhelming systems with SYN requests. Trained on a dataset of network connection attributes, the model learns patterns distinguishing normal activities from Neptune attacks. It leverages ensemble learning to enhance accuracy by aggregating predictions from multiple decision trees. Key features include robustness against noise and ability to handle large datasets efficiently. Evaluated using metrics like accuracy, precision, recall, and F1-score, the model aims to effectively identify and mitigate Neptune attacks, contributing to network security by preemptively alerting and protecting against malicious traffic.