Threat Detection and Mitigation for Tactile Internet Driven Consumer IoT-Healthcare System

With the rising adoption of Tactile Internet-driven Consumer Healthcare IoT, ensuring the security and reliability of sensitive healthcare data has become a paramount concern. Threat detection and mitigation in such dynamic and critical environments pose unique challenges. In this context, the main focus of this research is to propose a novel model named Hybrid Ensemble Learning Enabled Sigmoid-Cosine Integrated Pigeon Inspired Feature Selection Based Intrusion Detection (HSPFSID), specifically designed for threat detection and mitigation for Tactile Internet-driven Consumer Healthcare IoT. The proposed model employs a hybrid ensemble learning approach and introduces the innovative Sigmoid-Cosine integrated pigeon-inspired feature selection (SCIPIFS) algorithm. We conducted extensive experiments on four distinct datasets (IoT bot, NSL-KDD, CIC2017, and KDD99) under standard metric measures to evaluate their effectiveness. The results indicate that our proposed work outperforms existing approaches, emphasizing their significant potential in bolstering cyber security measures for healthcare IoT systems.