Hybrid dual-head IDS: A lightweight and explainable deep learning DDOS/DOS framework for zero-day and imbalanced attack detection in IoT networks

This publication has 31 references indexed in Scilit:

• Real-time explainable IoT security with machine learning and CTGAN-enhanced detection for resource-constrained devices
  Ad Hoc Networks, 2025
• ARLHNIDS-IoT: An accurate and robust lightweight hybrid-NIDS for IoT network security
  Computers & Security, 2025
• Network security based combined CNN-RNN models for IoT intrusion detection system
  Peer-to-Peer Networking and Applications, 2025
• Securing Industry 5.0: An explainable deep learning model for intrusion detection in cyber-physical systems
  Computers and Electrical Engineering, 2025
• Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks
  Applied Soft Computing, 2024
• Enhancing IoT cyber attacks intrusion detection through GAN-based data augmentation and hybrid deep learning models for MQTT network protocol cyber attacks
  Cluster Computing, 2024
• Towards a Deep Learning Approach for IoT Attack Detection Based on a New Generative Adversarial Network Architecture and Gated Recurrent Unit
  Journal of Network and Systems Management, 2024
• Generative Adversarial Network Models for Anomaly Detection in Software-Defined Networks
  Journal of Network and Systems Management, 2024
• A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem
  Ain Shams Engineering Journal, 2024
• Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review
  Journal of Cancer Survivorship, 2023