AI-driven Intrusion Detection Systems in SDN-Based IoT Networks: Evaluating and Defending Against Adversarial Attacks

AI-Driven Vulnerability Management Framework for the Internet of Things

This rapid growth of IoT has changed the landscape of today’s digital world by allowing devices to communicate effectively, especially in different fields like healthcare, smart cities, industrial control, and defense. Despite its advantages, IoT introduces significant security challenges due to device heterogeneity...

AI-Driven Intrusion Detection and Energy-Efficient Routing in Internet of Things (IoT) Networks

Autonomous Digital Twin Framework for Predictive Cyber Defense in Edge-IoT Networks

Detection of DDoS Attacks in LoRaWAN-based IoT Networks using Isolation Forest

Explainable AI-Based Intrusion Detection Systems for IoT Environments: A Systematic Literature Review

This paper aims to provide a foundational resource to guide future research on reliable, explainable, and practical IoT intrusion detection systems by identifying the problems addressed in current research and highlighting the limitations in the literature.

Data-Driven Detection of Multi-vector IoT DDoS Attacks across Network Layers

—As cyberattacks targeting Internet of Things (IoT) networks grow more sophisticated, the demand for models capable of accurately detecting and mitigating these threats becomes increasingly urgent existing detection systems often concentrate on a single attack surface which leads to critical blind spots in IoT network...