A DDoS Attack Detection Method Based on Machine Learning

Abstract

Distributed denial-of-service attack, also known as DDoS attack, is one of the most common network attacks at present. With the rapid development of computer and communication technology, the harm of DDoS attack is becoming more and more serious. Therefore, the research on DDoS attack detection becomes more important. Nowadays, some related research work has been done and some progress has been made. However, due to the diversity of DDoS attack modes and the variable size of attack traffic, there has not yet been a detection method with satisfactory detection accuracy at present. In view of this, this paper proposes a DDoS attack detection method based on machine learning, which includes two steps: feature extraction and model detection. In the feature extraction stage, the DDoS attack traffic characteristics with a large proportion are extracted by comparing the data packages classified according to rules. In the model detection stage, the extracted features are used as input features of machine learning, and the random forest algorithm is used to train the attack detection model. The experimental results show that the proposed DDoS attack detection method based on machine learning has a good detection rate for the current popular DDoS attack.