A Smart Approach for Early Detection of DDoS Attacks: Artificial Neural Network and Random Forest Hybridization

Advances in networking technology have made Distributed Denial of Service (DDoS) attacks a real danger to today’s networks. Using logical reasoning, the network flow circumstances may be classified as an attack or a routine state to mimic DDoS detection. This research builds an Artificial Intelligence (AI) system using current improvements in Detection System (DS) and Artificial Neural Network (ANN) algorithms advances. It examines User Datagram Protocol (UDP) foods, ping foods, Transmission Control Protocol (TCP) foods, and land attacks to better understand attack behavior. The categorization model for DDoS attacks is constructed using machine learning approaches. Once trained and evaluated, the model can identify unlabeled benign or malicious network data. Experiments reveal that Decision Tree (DT), Random Forest (RF), Naïve Bayes,…