The Internet of Things (IoT) networks, especially LoRaWAN networks, has allowed for the transmission of low power and long range for many smart applications. But the growing number of connected devices creates a huge security problem, as it includes threats from insiders, which are either compromised or malicious and a...
The rapid expansion of the Internet of Things has introduced significant security challenges, particularly blackhole attacks that disrupt network communication by malicious nodes dropping all received packets. This study presents a controlled case-study analysis of blackhole attack impact in an RPL-based IoT environmen...
The rapid growth of Internet of Things (IoT) deployment has increased exposure to Distributed Denial of Service (DDoS) attacks, while the latency and bandwidth costs of cloud-centric intrusion detection constrain real-time response. This paper presents a fog-based DDoS detection framework that combines XGBoost with inf...
This work shows that production‐grade, privacy‐preserving intrusion detection is feasible in IoT networks at the edge, and addresses the concerns of data privacy and non‐IID data distribution inherent to distributed IoT networks.
An AI-driven framework for intrusion detection and energy-efficient routing in IoT networks is proposed that uses machine-learning techniques to identify malicious network traffic and classify it as normal or abnormal and applies an energy-aware routing strategy to select communication paths.
The Gotham testbed dataset is presented, a device-level IoT network dataset generated using the open-source Gotham testbed, a virtualised smart city environment that preserves device-level traffic distributions and captures non-IID characteristics without artificial partitioning.
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