Elicitation and Analysis of Security Requirements and Patterns for IoT Based Health Monitor

Aruna, E. R.; Reddy, A. Rama Mohana; Sunitha, K. V. N.

doi:10.1007/978-981-15-3125-5_6

Cited by 1 publication

(3 citation statements)

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“…Since these health care systems are critical, it is important to ensure that they work efficiently, encompassing all the required non-functional requirements. Health care systems have compromised performance or security, resulting in the loss of critical health data and even threats and attacks by hackers, which may affect the life of patients (17). An IoT-oriented health care system generally has three layers, which are the perception layer, fog layer, and cloud layer (43)(44)(45).…”

Section: Methodsmentioning

confidence: 99%

“…The non-functional requirements of an IoT oriented health care system generally include security (17,42,44) privacy interoperability, scalability, reliability (17,44), accuracy, usability (17), performance (17,44,48), and maintainability (48). The system must possess these non-functional requirements since missing any critical non-functional requirement results in severe loss of healthcare data and other threats to patients' lives.…”

Section: Methodsmentioning

confidence: 99%

“…Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency (14)(15)(16). Thus, it is critical to ensure that these smart healthcare systems are extremely secure and perform reliably so that patients' sensitive data is not only kept protected from manipulation and attacks but also transferred entirely and safely in a timely fashion (17)(18)(19). In order to ensure that the IoT-oriented healthcare system possesses all these important non-functional requirements, it is necessary to extract all the non-functional requirements from the requirement document of the IoT-oriented healthcare system in the RE phase.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

Khurshid

Imtiaz

Boulila

et al. 2022

Front. Public Health

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Internet of Things (IoT) involves a set of devices that aids in achieving a smart environment. Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency. Currently, machine learning-based techniques are adopted to ensure security and other non-functional requirements in smart health care systems. However, no attention is given to classifying the non-functional requirements from requirement documents. The manual process of classifying the non-functional requirements from documents is erroneous and laborious. Missing non-functional requirements in the Requirement Engineering (RE) phase results in IoT oriented healthcare system with compromised security and performance. In this research, an experiment is performed where non-functional requirements are classified from the IoT-oriented healthcare system's requirement document. The machine learning algorithms considered for classification are Logistic Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), K-Nearest Neighbors (KNN), ensemble, Random Forest (RF), and hybrid KNN rule-based machine learning (ML) algorithms. The results show that our novel hybrid KNN rule-based machine learning algorithm outperforms others by showing an average classification accuracy of 75.9% in classifying non-functional requirements from IoT-oriented healthcare requirement documents. This research is not only novel in its concept of using a machine learning approach for classification of non-functional requirements from IoT-oriented healthcare system requirement documents, but it also proposes a novel hybrid KNN-rule based machine learning algorithm for classification with better accuracy. A new dataset is also created for classification purposes, comprising requirements related to IoT-oriented healthcare systems. However, since this dataset is small and consists of only 104 requirements, this might affect the generalizability of the results of this research.

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