An innovative intrusion detection framework using GAN-augmented Deep Ensemble Neural Network for cross-domain IoT–cloud security

Abstract

The rising popularity of smart cities and their impact across multiple sectors, including healthcare, transportation, and industry, is due to the rapid expansion of the Internet of Things (IoT). The growing popularity of IoT environments has made them susceptible to a wide array of cybersecurity hazards, such as denial-of-service (DoS), brute-force, and malicious access assaults. Robust intrusion detection and forensic investigation approaches must be developed to counter the aforementioned hazards. These frameworks primarily benefit from authentic and well-organized datasets for successful training and validation.

This study introduces an innovative Generative Adversarial Networks GAN-enhanced Deep Ensemble Neural Network (DENNW) framework designed specifically for cross-domain intrusion detection in cloud and IoT environments. This method significantly improves intrusion detection across various datasets by combining a multi-source learning architecture with GAN-based oversampling to address class imbalance. The Bot-IoT and CSE-CIC-IDS-2018 datasets are used in this research, containing both real and synthetic network traffic, covering a broad range of IoT and cloud-related incidents. The proposed GAN-based DENNW framework outperforms existing cloud-based approaches that use similar measures, providing comprehensive class-wise metric evaluation with 97.22% overall accuracy, surpassing many current studies. Although the DENNW framework achieves 93% accuracy with detailed class-wise analysis, the suggested approach enhances operational efficiency in the IoT sector. The results highlight that the proposed framework for protecting emerging IoT–cloud systems is adaptable and practical.