These findings demonstrate that Edge-ZTA provides an efficient, privacy-preserving, and scalable cybersecurity framework capable of mitigating sophisticated multi-stage cyberattacks while satisfying the stringent performance requirements of next-generation Industrial IoT infrastructures.
This thesis presents an efficient and adversarial-resilient IDS framework to detect existing and previously unseen cyberattacks for SDN-based IoT networks.
This study proposes an AI driven vulnerability management framework that integrates anomaly detection techniques using machine learning to enhance proactive threat identification and mitigation in IoT ecosystems, and demonstrates that AI based models significantly outperform traditional methods in detecting previously...
This review paper critically examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance information security within Cloud-IoT networks, focusing on hybrid Deep Learning models (CNN-LSTM), predictive analytics, and automated threat response mechanisms.
The findings of this review show that proactive, cooperative, and self-healing security mechanisms are more effective than traditional reactive approaches in IoT environments and provide recommendations for developing scalable, energy-efficient, and privacy-preserving IoT security frameworks for future research.
This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.
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