A BiLSTM-Based DDoS Attack Detection Method for Edge Computing

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 De...

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Bibliographic Details
Published in:Energies (Basel) 2022-11, Vol.15 (21), p.7882
Main Authors: Zhang, Yiying, Liu, Yiyang, Guo, Xiaoyan, Liu, Zhu, Zhang, Xiankun, Liang, Kun
Format: Article
Language:English
Subjects:
Algorithms
attack detection
bidirectional long short-term memory
Data security
Deep learning
Denial of service attacks
distributed denial of service attacks
edge computing
Edge detection
Electric power grids
Information retrieval
Internet of Things
Long short-term memory
Machine learning
Methods
Neural networks
power Internet of Things
Prevention
Recurrent neural networks
Security
Servers
Time lag
Traffic
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15217882