Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
Recently, healthcare monitoring systems have emerged as significant tolls for constant monitoring of patient's physiological characteristics. These systems use implanted sensors. IoT (Internet of Things) have revolutionized healthcare systems where health care equipment's are equipped with...
Saved in:
Published in | Measurement. Sensors Vol. 30; p. 100929 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.12.2023
Elsevier |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Recently, healthcare monitoring systems have emerged as significant tolls for constant monitoring of patient's physiological characteristics. These systems use implanted sensors. IoT (Internet of Things) have revolutionized healthcare systems where health care equipment's are equipped with many sensors that actively collect data from patients and pass it on to cloud based storages using gateway sensors. Securing data have been significant barriers in many applications as false information get injected, or important information are modified or stolen at different phases of health care systems dependent on IoT. The attacks can also result in fatalities making it imperative to secure IoT based health care systems. A Hybrid technique combining MOAES (Modified Optimal Advanced Encryption Standard) with CM (Chaotic Map) Encryptions called HMOAES-CM technique is proposed. This technique can be helpful in securely accessing the patient data over online mode, and in addition, the data sharing can be performed in an encrypted form for the necessary targets of stakeholders. The proposed authentication approach is aimed at IoT, which is resilient to all kinds of network attacks and its implementation is also simpler. Comparing the suggested work to similar works, the level of evaluation is much improved. |
---|---|
ISSN: | 2665-9174 2665-9174 |
DOI: | 10.1016/j.measen.2023.100929 |