Deep Learning-Based Network Security Situation Prediction for Sensor-Enabled Networks

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.

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.

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.

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.