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Cybersecurity Situational Awareness Model using Improved LSTM-Informer

Xin Zhou, Bo Li
International Journal of Computer Science and Information Technology, (2024), Vol.2, No.3, pp.37-49
Published: May 28, 2024
DOI: 10.62051/ijcsit.v2n3.05
PDF: Download Full Text PDF
Abstract

To address the problem of low prediction accuracy in current network security situational prediction models, this study proposes an improved LSTM-Informer model. The study first employs the Empirical Mode Decomposition (EMD) technique to preprocess network security situational data, extracting stable Intrinsic Mode Functions (IMFs). These IMFs are then used as inputs to the Long Short-Term Memory (LSTM) model, which effectively enhances the accuracy and stability of LSTM in handling time series data. Secondly, this paper introduces a coding-decoding mechanism that is based on Lightweight Gradient Boosting Machine (LGB) to efficiently encode time series data. The encoded data is then fed into the Informer model, which optimizes the flow of information between LSTM and Informer, significantly improving the model's ability to predict future network security situational trends. Finally, the Particle Swarm Optimization (PSO) algorithm is utilized to optimize model parameters, enhancing prediction accuracy. The experimental results indicate that the proposed model outperforms in terms of evaluation metrics such as Mean Square Error and Mean Absolute Error, with a best-fit rate of 99.36%. When applied to real network situational data from the China National Vulnerability Database (CNVD), the model demonstrates good predictive performance, effectively addressing the issue of low accuracy in network security situational prediction.

Keywords: Cybersecurity, Situation Prediction, Long Short-Term Memory, Informer, Empirical Mode Decomposition, Lightweight Gradient Boosting Machine
APA Citation: Xin Zhou, Bo Li (2024). Cybersecurity Situational Awareness Model using Improved LSTM-Informer. International Journal of Computer Science and Information Technology, 2(3), 37-49. https://doi.org/10.62051/ijcsit.v2n3.05

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