AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results indicate that contextual representations from BERT, sequential dependency modelling through LSTM, and adaptive feature weighting through Bahdanau Attention provide complementary capabilities for fake news classification.
Experimental results demonstrate that the proposed framework achieved strong and balanced predictive performance and demonstrated improvements in several classification metrics compared with the previously developed BFA-based Hybrid TabNet-DNN model.
These findings demonstrate that stacking ensemble learning is a practical and computationally efficient alternative to complex deep learning architectures for SDN intrusion detection, with strong potential for scalable and real-time cybersecurity deployment in modern network environments.
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