A Methodology to Detect Distributed Denial of Service Attacks

Distributed denial of service (DDoS) attacks is one the most destructive cyber which target availability system when legitimate users try to access system. Not only computers, but also growing number smartphones as well Internet Things (IoT) devices are affected by DDoS attacks. There no well-known effectively stops or prevents Designing an effective detector with high accuracy low computational overhead still a very challenging task. In this paper, methodology, used detect and classify types attacks, proposed. Our methodology divided into three parts: pre-processing, feature selection, classification. First, pre-processing performed eliminate some features not suitable for our model. Second, significant selected using Information Gain, Gain Ratio, Correlation Coefficient, Relief. We declined from 87 20. Finally, various classifiers bening ones. The proposed on CIC-DDoS2019 dataset. experimental results show that pretty it compared leading methods in literature.