A BiLSTM-Based DDoS Attack Detection Method for Edge Computing

Yiying Zhang, Yiyang Liu (), Xiaoyan Guo, Zhu Liu, Xiankun Zhang and Kun Liang
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Yiying Zhang: College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China
Yiyang Liu: College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China
Xiaoyan Guo: Information and Communication Company, State Grid Tianjin Electric Power Company, Tianjin 300140, China
Zhu Liu: State Grid Information and Communication Industry Group Co., Ltd., Beijing 100070, China
Xiankun Zhang: College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China
Kun Liang: College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China

Energies, 2022, vol. 15, issue 21, 1-17

Abstract: With the rapid development of smart grids, the number of various types of power IoT terminal devices has grown by leaps and bounds. An attack on either of the difficult-to-protect end devices or any node in a large and complex network can put the grid at risk. The traffic generated by Distributed Denial of Service (DDoS) attacks is characterised by short bursts of time, making it difficult to apply existing centralised detection methods that rely on manual setting of attack characteristics to changing attack scenarios. In this paper, a DDoS attack detection model based on Bidirectional Long Short-Term Memory (BiLSTM) is proposed by constructing an edge detection framework, which achieves bi-directional contextual information extraction of the network environment using the BiLSTM network and automatically learns the temporal characteristics of the attack traffic in the original data traffic. This paper takes the DDoS attack in the power Internet of Things as the research object. Simulation results show that the model outperforms traditional advanced models such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) in terms of accuracy, false detection rate, and time delay. It plays an auxiliary role in the security protection of the power Internet of Things and effectively improves the reliability of the power grid.

Keywords: distributed denial of service attacks; attack detection; edge computing; bidirectional long short-term memory; power Internet of Things (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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