A Hybrid Deep Learning Method for Network Attack Prediction

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

Precise real-time prediction of the number of future network attacks cannot only prompt cloud infrastructures to fast respond to them and protect network security, but also prevents economic and business losses. In recent years, neural networks, e.g., Bi-direction Long and Short Term Memory (LSTM) and Temporal Convolutional Network (TCN), have been proven to be suitable for predicting time series data. Attention mechanisms are also widely used for the time series prediction. In this work, we propose a novel hybrid deep learning prediction method by combining the capabilities of a Savitzky-Golay (SG) filter, TCN, Multi-head self attention, and BiLSTM for the prediction of network attacks. This work first adopts a SG filter to eliminate noise in the raw data. It applies TCN to extract short-term features from the sequences. It then adopts multi-head self attention to capture intrinsic connections among features. Finally, this work adopts Bi-LSTM to extract bi-directional and long-term correlations in the sequences. Experimental results with a real-life dataset show that the proposed method outperforms several typical algorithms in terms of prediction accuracy.

Identifier

85142735904 (Scopus)

ISBN

[9781665452588]

Publication Title

Conference Proceedings IEEE International Conference on Systems Man and Cybernetics

External Full Text Location

https://doi.org/10.1109/SMC53654.2022.9945189

ISSN

1062922X

First Page

544

Last Page

549

Volume

2022-October

Grant

62073005

Fund Ref

National Natural Science Foundation of China

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