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Network Security Perception System Integrating Improved CNN Algorithm and Improved GRU Algorithm
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DOI：10.1109/ACCESS.2024.3489664.png)
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Abstract
En 中文
To enhance the accuracy and real-time response of network security-aware systems, the convolutional neural network is optimized by introducing exponentially weighted dempster-shafer evidence theory. Additionally, the gated recurrent unit is improved using an adaptive boosting algorithm. A novel network security-aware model integrating both methods is constructed. Experimental results show that the model achieves a detection accuracy of up to 94%, with the lowest values for miss rate, false positive rate, and false alarm rate being 7.84%, 2.47%, and 3.11%, respectively. This indicates that the proposed model provides an efficient and accurate solution for network security defense.
Keywords:
Network security
Convolutional neural networks
Accuracy
Mathematical models
Hidden Markov models
Adaptation models
Real-time systems
Heuristic algorithms
Detection algorithms
Convolutional neural network
gated recurrent unit
network security
perception
detection