DDoS attack detection method based on feature extraction of deep belief network

TL;DR

This paper proposes a DDoS attack detection method that combines deep belief network feature extraction with an LSTM model to accurately identify and predict network traffic anomalies caused by DDoS attacks.

Contribution

It introduces a novel combination of deep belief networks and LSTM for effective DDoS attack detection based on IP packet features.

Findings

Accurately predicts normal traffic trends

Identifies DDoS attack anomalies effectively

Suitable for diverse DDoS attack detection

Abstract

Distributed Denial of Service (DDOS) attack is one of the most common network attacks. DDoS attacks are becoming more and more diverse, which makes it difficult for some DDoS attack detection methods based on single network flow characteristics to detect various types of DDoS attacks, while the detection methods of multi-feature DDoS attacks have a certain lag due to the complexity of the algorithm. Therefore, it is necessary and urgent to monitor the trend of traffic change and identify DDoS attacks timely and accurately. In this paper, a method of DDoS attack detection based on deep belief network feature extraction and LSTM model is proposed. This method uses deep belief network to extract the features of IP packets, and identifies DDoS attacks based on LSTM model. This scheme is suitable for DDoS attack detection technology. The model can accurately predict the trend of normal network traffic, identify the anomalies caused by DDoS attacks, and apply to solve more detection methods about DDoS attacks in the future.