Classification and Analysis of Distributed Denial of Service Attacks using Machine Learning Techniques
P.S. Nandhini, S. Kuppuswami, C. Abinaya, R Boomika, Nandigam Poornima Deepthi · 2022
According to the recent statistics, DDoS attacks are responsible for the majority of overall network attacks. A distributed denial of service attack, sometimes known as a DDoS attack will destroy the server system resources such as the CPU, control and memory. As a result, the server will be too busy responding to a DDoS attack to provide service to the genuine users. Networks struggle to discern between malicious and lawful transmissions. Due to numerous variables like the rigidity, complexity and networking equipment, testing and deploying DDoS methods is difficult. Machine Learning are used to detect the DDoS attack but the best ML model to classify the DDoS attack is yet to be found. The proposed system makes use of supervised learning algorithm to find out the model that best classifies the DDoS attacks. Performance metrices have been calculated for various models. To ensure its suitability, the result was also compared with those from other models. As a result, accuracy of more than 96 percent have been achieved using Random Forest algorithm.