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Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network.

Biao Zhang¹, Mingqi Jia¹, Jiazhong Xu¹

¹Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin 150080, China.

Computational Intelligence and Neuroscience

|July 1, 2022

Summary

This study introduces an EMD-ELPSO-BiGRU model for enhanced network security situation prediction. The novel approach improves prediction accuracy and algorithm convergence speed for better cybersecurity insights.

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Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Accurate network security situation prediction is crucial for proactive defense.
Existing prediction algorithms often face challenges with accuracy and convergence speed.

Purpose of the Study:

To develop a novel combined prediction model for improving network security situation prediction accuracy and algorithm convergence speed.
To optimize the BiGRU neural network using an improved particle swarm optimization algorithm.

Main Methods:

Empirical Mode Decomposition (EMD) was used to decompose network security data sequences into intrinsic mode functions.
An enhanced Particle Swarm Optimization (ELPSO) algorithm with cooperative updates and learning strategies was developed to optimize BiGRU hyperparameters.
A combined EMD-ELPSO-BiGRU model was constructed to predict each intrinsic mode function, with results superimposed for the final prediction.

Main Results:

The ELPSO algorithm demonstrated superior optimization performance.
The EMD-ELPSO-BiGRU model achieved higher prediction accuracy compared to traditional methods.
A significant improvement in the convergence speed of the prediction algorithm was observed.

Conclusions:

The proposed EMD-ELPSO-BiGRU model offers a more accurate and efficient approach to network security situation prediction.
The ELPSO algorithm provides a robust method for hyperparameter optimization in neural networks.
This research contributes to advancing predictive capabilities in cybersecurity.