Intrusion Detection Model Based on SAE and BALSTM

To solve the problems of low detection accuracy, high false positive rate and unbalanced network data sets in the high-dimensional massive data environment of traditional intrusion detection model, an intrusion detection model based on improved Stack autoencoder (SAE) and bidirectional feature atten...

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Bibliographic Details

Published in:	2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) pp. 1192 - 1197
Main Authors:	Jiajia, Fan, Jiangfeng, Xu, Junfeng, Zhang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 28.06.2021
Subjects:	Attention mechanism Bidirectional short-time memory network Computational modeling Computer applications Conferences Feature extraction Improved stack autoencoder Intrusion detection Simulation Smote-Tomek Standardization
Online Access:	Get full text
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