AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
The rapid growth of the Internet of Things (IoT) has created a highly interconnected digital environment, but this expansion has also introduced complex security risks, especially at the application layer where most user-level operations occur. Many existing intrusion detection systems (IDS) are unable to cope with...
A new architecture based on deep learning methods for binary classifications and featuring hybridization of multi-layer 1D-CNNs with LSTM networks provides a robust, stable, and scalable solution for mitigating DDoS threats in modern IoT ecosystems.
A Hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for effective IoT malware detection is proposed, which enhances detection capability for both known and zero-day attacks.
The proliferation of Internet of Things (IoT) networks has exacerbated security threats, especially in scenarios with limited computational resources, where traditional intrusion detection systems cannot be deployed. Deep learning (DL) methods have powerful detection performance but are often too large for real-world d...
A novel stacking model is proposed to identify attacks in the IoT environment by integrating four neural networks: deep neural network (DNN), recurrent neural network (RNN), RNN, long short-term memory (LSTM) and gated recurrent unit (GRU) with hyper-parameter tuned Light gradient boosting machine (BTE-LGBM).
As digital devices evolve quickly and cyberattacks grow more diverse, the Internet of Things (IoT) ecosystem faces a much higher security risk than before. An AI-based security system is commonly used for post-attack mitigation. However, this approach has several issues, such as high computational load, data imbalance,...
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