The model leverages the strengths of both CNNs and BiLSTM networks to effectively capture spatial and temporal patterns in network traffic data. Trained and evaluated the model using a comprehensive dataset of cyber attacks. Demonstrated the model’s effectiveness in accurately classifying different types of cyber attacks through experimental results. The model achieved a high accuracy of 99%. Contributed to enhancing network security and mitigating cyber threat risks by accurately identifying and classifying cyber attacks. Provided a foundation for developing advanced and robust cybersecurity solutions through the innovative hybrid CNN-BiLSTM model.
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The model leverages the strengths of both CNNs and BiLSTM networks to effectively capture spatial and temporal patterns in network traffic data. We trained and evaluated the model using a comprehensive dataset of cyber attacks. The model achieved a high accuracy of 99%.
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Bishal77/Hybrid-CNN-BiLSTM-architecture-for-detecting-multi-step-cyber-attack
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The model leverages the strengths of both CNNs and BiLSTM networks to effectively capture spatial and temporal patterns in network traffic data. We trained and evaluated the model using a comprehensive dataset of cyber attacks. The model achieved a high accuracy of 99%.
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