Exploiting Security Issues in Human Activity Recognition Systems (HARSs)
Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/6/315 |
| _version_ | 1797594197777186816 |
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| author | Sofia Sakka Vasiliki Liagkou Chrysostomos Stylios |
| author_facet | Sofia Sakka Vasiliki Liagkou Chrysostomos Stylios |
| author_sort | Sofia Sakka |
| collection | DOAJ |
| description | Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application. |
| first_indexed | 2024-03-11T02:20:14Z |
| format | Article |
| id | doaj.art-403b1333a7b04d99a7d3da907862072f |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T02:20:14Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-403b1333a7b04d99a7d3da907862072f2023-11-18T10:54:24ZengMDPI AGInformation2078-24892023-05-0114631510.3390/info14060315Exploiting Security Issues in Human Activity Recognition Systems (HARSs)Sofia Sakka0Vasiliki Liagkou1Chrysostomos Stylios2Department of Informatics and Telecommunications, University of Ioannina, 471 00 Arta, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 471 00 Arta, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 471 00 Arta, GreeceHuman activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application.https://www.mdpi.com/2078-2489/14/6/315human activity recognitionsecurityprivacysensorswearables |
| spellingShingle | Sofia Sakka Vasiliki Liagkou Chrysostomos Stylios Exploiting Security Issues in Human Activity Recognition Systems (HARSs) Information human activity recognition security privacy sensors wearables |
| title | Exploiting Security Issues in Human Activity Recognition Systems (HARSs) |
| title_full | Exploiting Security Issues in Human Activity Recognition Systems (HARSs) |
| title_fullStr | Exploiting Security Issues in Human Activity Recognition Systems (HARSs) |
| title_full_unstemmed | Exploiting Security Issues in Human Activity Recognition Systems (HARSs) |
| title_short | Exploiting Security Issues in Human Activity Recognition Systems (HARSs) |
| title_sort | exploiting security issues in human activity recognition systems harss |
| topic | human activity recognition security privacy sensors wearables |
| url | https://www.mdpi.com/2078-2489/14/6/315 |
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