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2000
Volume 15, Issue 3
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Aims and Background

Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection.

Objectives and Methods

Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks (CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization (GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules.

Results and Discussion

The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data.

Conclusion

In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

© 2025 Bentham Science Publishers
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2026-02-16

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Deep Learning-Based Network Security Situation Prediction for Sensor-Enabled Networks
Int J Sensors Wirel Commun Control 15, 232 (2025); https://doi.org/10.2174/0122103279332710240919091605
/content/journals/swcc/10.2174/0122103279332710240919091605
/content/journals/swcc/10.2174/0122103279332710240919091605
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  • Article Type:     Research Article
Keyword(s): CW-RNN; deep learning; GWO; Neural network; Security threat detection; situation prediction

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