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A survey and taxonomy of program analysis for IoT platforms.

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Title: A survey and taxonomy of program analysis for IoT platforms.
Authors: A. Hamza, Alyaa1,2 (AUTHOR) alyaashams12@yahoo.com, Abdel-Halim, Islam T.3 (AUTHOR) islamhalim@yahoo.com, Sobh, Mohamed A.2 (AUTHOR) mohamed.sobh@eng.asu.edu.eg, Bahaa-Eldin, Ayman M.4 (AUTHOR) ayman.bahaa@eng.asu.edu.eg
Source: Ain Shams Engineering Journal. Dec2021, Vol. 12 Issue 4, p3725-3736. 12p.
Subjects: Internet of things, Taxonomy, Key performance indicators (Management), Security systems, Malware
Abstract: Heterogeneity in the Internet of Things (IoT) environment is a critical issue for supporting security and privacy. IoT environment has become an open invitation to hackers to control and attack connected IoT devices. So, it has become essential to face security issues & challenges in IoT due to IoT applications' rapid development. Program Analysis (PA) is a method that focuses on defending against attacks on the systems implemented to detect malware applications. It is also responsible for adequately analyzing applications' behavior to provide security and privacy. This survey has been introduced carefully based on the systematic literature reviews (SLR) guidelines to provide a survey and taxonomy of the PA with its related topics: the sensitivity of analysis and characteristics of analysis. It presents a new classification of PA techniques. This classification has been created by examining the implemented security analysis systems (SAS) that detect various malware applications. More importantly, this survey presents the three types of SAS that used PA methods for the first time. Also, the related surveys, the performance metrics of PA and IoT Security Issues and Challenges have been discussed. Finally, future directions of the PA have been discussed. [ABSTRACT FROM AUTHOR]
Copyright of Ain Shams Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: A survey and taxonomy of program analysis for IoT platforms.
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  Data: Heterogeneity in the Internet of Things (IoT) environment is a critical issue for supporting security and privacy. IoT environment has become an open invitation to hackers to control and attack connected IoT devices. So, it has become essential to face security issues & challenges in IoT due to IoT applications' rapid development. Program Analysis (PA) is a method that focuses on defending against attacks on the systems implemented to detect malware applications. It is also responsible for adequately analyzing applications' behavior to provide security and privacy. This survey has been introduced carefully based on the systematic literature reviews (SLR) guidelines to provide a survey and taxonomy of the PA with its related topics: the sensitivity of analysis and characteristics of analysis. It presents a new classification of PA techniques. This classification has been created by examining the implemented security analysis systems (SAS) that detect various malware applications. More importantly, this survey presents the three types of SAS that used PA methods for the first time. Also, the related surveys, the performance metrics of PA and IoT Security Issues and Challenges have been discussed. Finally, future directions of the PA have been discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Ain Shams Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=153679878
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      – SubjectFull: Key performance indicators (Management)
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              Text: Dec2021
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