AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
This study examines a one-dimensional Convolutional Neural Network and a hybrid model, investigating how both architectures can detect network attacks in binary and multiclass classification settings, and provides actionable insights for practitioners choosing between deep learning and classical approaches under real-world NIDS deployment constraints.
An Enhanced Multi-Model Ensemble Network Intrusion Detection System (EME-NIDS), a deep meta-learning system that combines five different heterogeneous learning paradigms, including Convolutional Neural Networks, Dense Neural Networks, Transformers, XGBoost, and Random Forests is introduced.
SmartVille is introduced, a framework for formulating and studying deep learning-based NID under online, open-world, and multi-modal assumptions, and providing a principled way to design, train, and benchmark adaptive NID models under realistic assumptions while separating the theoretical contribution from its open-source implementation.
The findings confirm that the proposed IDSaaS framework provides an efficient, scalable, and adaptive solution for real-time cloud intrusion detection and significantly enhances the reliability and resilience of modern cloud and industrial cybersecurity infrastructures.
The Hybrid Autoencoder–TabTransformer framework provides an effective intrusion detection solution that demonstrates strong performance under the evaluated experimental conditions and comparative analysis with existing deep learning‐based intrusion detection approaches confirms the superior and balanced performance of the proposed method.
ShieldDRLNet is a hybrid deep reinforcement learning framework for proactive cloud-network intrusion detection that employs a convolutional neural network and a long short-term memory encoder to obtain a spatiotemporal traffic representation and uses a Double Deep Q-Network agent for adaptive sequential decision-making.
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