Network Attack Prediction With Hybrid Temporal Convolutional Network and Bidirectional GRU

Precise and real-time prediction of future network attacks can not only prompt cloud infrastructures to fast respond and protect network security but also prevents economic and business losses. In recent years, neural networks, e.g., bidirectional gated recurrent unit (Bi-GRU) network and temporal convolutional network (TCN), have been proven to be suitable for predicting time-series data. Attention mechanisms are also widely used for the prediction of the time series of network attacks. This work proposes a hybrid deep learning prediction method that combines the capabilities of Savitzky-Golay (SG) filter, TCN, multihead self-attention, and Bi-GRU (STMB) for the prediction of network attacks. This work first adopts an SG filter to smooth possible outliers and noise in network attack traffic data. It applies TCN to extract abstract features from 1-D time series to make full use of data. It then adopts multihead self-attention to capture internal correlations among multidimensional features, by increasing the weights of key features and reducing those weight of non-key features, making that STMB captures important features adaptively. Finally, this work adopts Bi-GRU to extract bidirectional and long-term correlations in the time series to improve the prediction accuracy. This work also utilizes a hybrid algorithm named genetic simulated-annealing-based particle swarm optimizer to determine the hyperparameter setting of STMB. Experimental results with real-life data sets show that STMB outperforms several commonly used algorithms in terms of prediction accuracy.