An Automated Cyber Intrusion Prediction Model Using Deep Learning to Resilient in Cyber Threat for the Royal Thai Air Force
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Abstract
The Royal Thai Air Force was one of the nation’s Critical Information Infrastructure (CII) Organizations and has a record of cyber intrusions continue throughout the year. Therefore, researchers presented a new automated cyber intrusion prediction model using deep learning to resilient in cyber threat for the Royal Thai Air Force. It was an extension of the Looking Back Algorithm to increase the accuracy of the predictive model. In order to predict the future of the Air Force’s cyber threat patterns, researchers used cyber intrusion datasets from the Air Force that ranging from January 2021 to December 2021 with a total of 241,148 entries. We applied techniques such as RNN, LSTM, GRU, Bi-LSTM Deep Learning (DL). We developed the new cyber intrusion prediction model with name Bi-LSTM Looking Back Risk: Bi-LSTM-LBR. However, the developed model had high accurate result on test dataset that compared to other predictive models. In addition, prediction results had a Mean Absolute Error (MAE) was 0.038, a Mean Square Error (MSE) was 0.010 and a Root Mean Square Error (RMSE) was at 0.102.
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References
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