An Intrusion Detection Mechanism for Secured IoMT Framework Based on Swarm-Neural Network
| Autor: | Varun G. Menon, Sandeep Verma, Sudarshan Nandy, Mohammad Ayoub Khan, Mainak Adhikari |
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| Rok vydání: | 2022 |
| Předmět: |
Artificial neural network
business.industry Computer science Data Collection Internet of Things Swarm behaviour Intrusion detection system computer.software_genre Computer Science Applications Intelligent agent Health Information Management Privacy Humans The Internet Neural Networks Computer Enhanced Data Rates for GSM Evolution Electrical and Electronic Engineering business Delivery of Health Care computer Biotechnology Vulnerability (computing) Computer network Data transmission |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 26:1969-1976 |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2021.3101686 |
| Popis: | The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT applications and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset. |
| Databáze: | OpenAIRE |
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