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A network security situation assessment method based on adversarial deep learning
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DOI：10.1016/j.asoc.2021.107096.png)
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Aiming at the poor performance and flexibility of traditional assessment methods of network security in dealing with a large number of network attack data, this paper proposes a network security situation assessment method based on adversarial deep learning. Firstly, establish the Deep Autoencoder-Deep Neural Network (AEDNN) model based on Deep Autoencoder (DAE) and Deep Neural Network (DNN). Conduct feature learning on the DAE network and apply the DNN network as a network attacks classifier. In the training process, for considering the results of feature learning in DAE, this paper builds an adversarial training process by changing the training weights. Besides, to increase the performance of the model on the minority network attacks, the Under-Over Sampling Weighted (UOSW) algorithm is designed; Finally, conduct model testing and calculate the network security situation value. The compared results of other models show the proposed model is more accurate for identifying network attacks and can evaluate the network situation more comprehensively and flexibly.(c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Network security situation assessment
Network attacks
Deep learning
Autoencoder
Adversarial training
Data resampling
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6.6
论文数:
1.4W
被引数:
4.8W
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