Next-Generation Cyber Threat Detection: Leveraging CNN-GRU Architecture and Honey Badger Algorithm Optimization for Network Security
Keywords:
Classification Accuracy, Cyber Threat Detection, Convolutional Neural Network, Feature Extraction, Gated Recurrent Unit, Honey Badger Optimization Algorithm, Network Security, OptimizationAbstract
The rising significance of Internet and Communication Technology (ICT) has resulted in the increased volume of transmitted data. The attackers gain unauthorized access to the network data and inject potential threats into the system for stealing or manipulating the data hence it is considered as a major obstacle for attack detection. This study offers an effective framework that combines the Gated Recurrent Unit (GRU) technique with a hybrid Convolutional Neural Network (CNN). This work's primary goal is to identify cyberthreats and categorize various attacks on security. The future hybrid CNN-GRU approach leverages the strengths of both CNN and GRU algorithms for attack detection. The hybrid model is optimized using a Honey Badger Optimization Algorithm (HBOA). The HBOA is used to adjust the model parameters in order to improve the performance metrics for different sorts of cyberattacks, such as Precision, Recall, f1-score, and others. The hybrid model is intended to extract high-level features from the network data. Simulation analysis is used to assess and validate the hybrid CNN-GRU model's effectiveness and support. The CNN-GRU model produces the classification output with an accuracy of 94.22% in terms of identifying various security attacks. The model is trained using input data taken from the CICIDS-2017 dataset.
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