DDoS Classification Through Web Application: Leveraging Algorithms for Cybersecurity

This study and project proposes a method for classifying Distributed Denial of Service (DDoS) attacks using the Random Forest Algorithm, with a focus on determining measurement metrics for distinguishing between DDoS attacks and benign network activity. DDoS attacks represent a significant threat to network availability and security, necessitating robust classification mechanisms. The Random Forest Algorithm is employed due to its effectiveness in handling large pattern datasets and its ability to detect complex attack patterns. By analyzing various measurement metrics such as packet rate, packet size distribution, and traffic volume, features indicative of DDoS attacks are identified. These features are then used to train the Random Forest model to classify incoming network traffic as either DDoS or Benign. Evaluation of the classification performance is conducted using metrics such as accuracy, precision, recall, and F1 score, providing insights into the effectiveness of the proposed approach. Experimental results demonstrate the ability of the Random Forest Algorithm to accurately classify DDoS attacks while minimizing false positives, thereby enhancing network security and mitigating potential disruptions to web services.