Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

• Published in: The Journal of supercomputing 2022, Vol.78 (13), p.14866-14891
• Main Authors: Nayak, Janmenjoy, Meher, Saroj K., Souri, Alireza, Naik, Bighnaraj, Vimal, S.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bayesian analysis; Biosensors; Compilers; Computer Science; Cybersecurity; Decision making; Deep Learning; Emergency medical services; Emergency response; Health care; Health services; Internet of medical things; Interpreters; Machine learning; Medical electronics; Medical equipment; Minimum cost; Optimization; Parallel Computing in Biomed Sciences & Healthcare; Privacy; Processor Architectures; Programming Languages; Reforming
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-022-04453-z

The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare industry by leveraging its capabilities to improve personalized healthcare services by enabling seamless communication of medical data. IoMT facilitates prompt emergency responses and provides improved quality of medical services with minimum cost. With the advancement of modern technology, progressively ubiquitous medical devices raise critical security and data privacy concerns through resource constraints and open connectivity. Vulnerabilities in IoMT devices allow unauthorized access for potential entry into healthcare and sensitive personal data. In addition, the patient may experience severe physical damage with the attack on IoMT devices. To provide security to IoMT devices and privacy to patient data, we have proposed a novel IoMT framework with the hybridization of Bayesian optimization and extreme learning machine (ELM). The proposed model derives encouraging performance with enhanced accuracy in decision-making process compared to similar state-of-the-art methods.