Improving internet of health things security through anomaly detection framework using artificial intelligence driven ensemble approaches.

Cybersecurity incidents have become an increasing difficulty for the medical field as the extensive overview of technology in the health care systems. In the past few years, the amount of attacks has improved quickly in health care, and it is currently between the areas mainly targeted by cyberattacks globally. Internet of Health Things (IoHT) applications and devices swiftly developed recently, becoming widely vulnerable to cyberattacks as the devices are heterogeneous and smaller. Furthermore, IoHT must include devices utilized in the healthcare field. A robust cyber-attack detection method is crucial in the IoHT environment to reduce safety risks and protect devices from cyberattacks. Using machine learning (ML) techniques, AI-driven anomaly detection improves the detection of irregular patterns in large datasets, improving accuracy across fields like cybersecurity and healthcare. Artificial intelligence (AI)-based deep learning (DL) and ML models excel in adapting, learning, and detecting unknown attack behaviours. This study develops an Enhancing Internet of Health Things Security through Cyberattack Detection Using Serial Exponential Golf Optimization (EIoHTSCD-SEGO) technique. The EIoHTSCD-SEGO technique's key intention is automatically classifying anomaly detection using AI-based data science approaches. Initially, the EIoHTSCD-SEGO technique performs pre-processing stages at two levels: feature vector using the TF-IDF model and normalization using min-max to convert input data into a uniform format. Furthermore, an ensemble of DL classifiers, namely the recurrent neural network (RNN) model, bidirectional long short-term memory (BiLSTM) method, and kernel extreme learning machine (KELM) technique, is utilized for the classification of cyber-attacks. Finally, the serial exponential golf optimization algorithm (SEGOA) method is implemented to optimize the hyperparameter tuning of ensemble DL models. The simulation analysis of the EIoHTSCD-SEGO technique is performed using the ECU-IoHT benchmark dataset. The performance validation of the EIoHTSCD-SEGO technique portrayed a superior accuracy value of 99.33% over existing models.