PGFit: Static permission analysis of health and fitness apps in IoT programming frameworks

Nobakht, Mehdi; Sui, Yulei; Seneviratne, Aruna; Hu, Wen

doi:10.1016/j.jnca.2019.102509

Cited by 13 publications

(13 citation statements)

References 3 publications

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“…E-health cloud computing systems are a low-cost method for transporting health services. Although numerous studies have been conducted on e-health cloud computing systems globally, the effectiveness of e-health cloud computing systems remains widely debated [ 8 , 9 , 10 , 11 ]. Nevertheless, e-health cloud computing system applications may revolutionize the healthcare industry in the future.…”

Section: Introductionmentioning

confidence: 99%

“…Companies such as Aetna and IBM's Active Health Management subsidiary have established novel clinical Life 2021, 11, 310. https://doi.org/10.3390/life11040310 https://www.mdpi.com/journal/life data management systems according to the structure of cloud computing [6]. The aim of the collaborative care solution is to provide controlled access to various types of clinical patient data from various sources, such as medication records, lab data, and claims as well as electronic medical records [1,7,8]. Microsoft and Google services such as Microsoft HealthVault and Google Health focus on novel services for storing medical records [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…The aim of the collaborative care solution is to provide controlled access to various types of clinical patient data from various sources, such as medication records, lab data, and claims as well as electronic medical records [1,7,8]. Microsoft and Google services such as Microsoft HealthVault and Google Health focus on novel services for storing medical records [7,8]. E-health cloud computing systems are preferable to e-health systems because of their agility and portability, and because they can be applied in remote areas with limited access to general medical services.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the E-Health Cloud Computing Systems Adoption in Taiwan’s Healthcare Industry

Chang

Pan

et al. 2021

Life

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Although the electronic health (e-health) cloud computing system is a promising innovation, its adoption in the healthcare industry has been slow. This study investigated the adoption of e-health cloud computing systems in the healthcare industry and considered security functions, management, cloud service delivery, and cloud software for e-health cloud computing systems. Although numerous studies have determined factors affecting e-health cloud computing systems, few comprehensive reviews of factors and their relations have been conducted. Therefore, this study investigated the relations between the factors affecting e-health cloud computing systems by using a multiple criteria decision-making technique, in which decision-making trial and evaluation laboratory (DEMATEL), DANP (DEMATEL-based Analytic Network Process), and modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approaches were combined. The intended level of adoption of an e-health cloud computing system could be determined by using the proposed approach. The results of a case study performed on the Taiwanese healthcare industry indicated that the cloud management function must be primarily enhanced and that cost effectiveness is the most significant factor in the adoption of e-health cloud computing. This result is valuable for allocating resources to decrease performance gaps in the Taiwanese healthcare industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the E-Health Cloud Computing Systems Adoption in Taiwan’s Healthcare Industry

Chang

Pan

et al. 2021

Life

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“…The proposed HSAS-MD uses two techniques, which are MCT and DL. The following figures [ 9 , 10 , 11 , 15 , 16 , 17 , 18 ] are configured to represent the results of the research questions.…”

Section: Implementation Evaluation and Discussion Of Hsas-mdmentioning

confidence: 99%

“…Some SASs use static analysis such as [ 9 , 10 , 11 ], which analyzes the program without executing; this method has drawbacks, as some data may be lost during the analysis phase. Other SASs use dynamic analysis such as [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

Hamza

Halim

Sobh

et al. 2022

Sensors

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Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria.

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A Two-Fold Study to Investigate Users’ Perception of IoT Information Sensitivity Levels and Their Willingness to Share the Information

Gupta

Kaplan

Nygaard

et al. 2022

Communications in Computer and Information Science

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PGFit: Static permission analysis of health and fitness apps in IoT programming frameworks

Cited by 13 publications

References 3 publications

Evaluating the E-Health Cloud Computing Systems Adoption in Taiwan’s Healthcare Industry

Evaluating the E-Health Cloud Computing Systems Adoption in Taiwan’s Healthcare Industry

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

A Two-Fold Study to Investigate Users’ Perception of IoT Information Sensitivity Levels and Their Willingness to Share the Information

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