Security-Aware Provenance for Transparency in IoT Data Propagation
A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of data collected from numerous sources. A significant concern in IoT systems arises when end-users do not have sufficient transparency and are unaware of any potential data manipula...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10138384/ |
| _version_ | 1797808504554127360 |
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| author | Fariha Tasmin Jaigirdar Boyu Tan Carsten Rudolph Chris Bain |
| author_facet | Fariha Tasmin Jaigirdar Boyu Tan Carsten Rudolph Chris Bain |
| author_sort | Fariha Tasmin Jaigirdar |
| collection | DOAJ |
| description | A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of data collected from numerous sources. A significant concern in IoT systems arises when end-users do not have sufficient transparency and are unaware of any potential data manipulation and risk in each step involved in data propagation. One potential solution is to integrate security metadata in IoT-based security-aware provenance graphs that provides better transparency with security awareness at each step of data propagation. In this paper, we integrate security metadata into the provenance graph with predefined security policies. We design a hypothetical IoT-Health scenario with possible threats: node cloning, fault packet injection, denial of service, unauthorized access, and malicious code injection. We simulate these threats in six cases to identify relevant risks. Our findings show how a security-aware provenance graph can offer end users greater transparency and security awareness by identifying failed signature verification (case 1), denial of service (case 2), unauthorized access (case 3), intrusion detection (case 4), missing WAF (case 5), and permission violation (case 6). We evaluate the transparency through obtaining authentication, integrity, availability and detecting underlying threats. Accordingly, this study promotes better risk assessment and decision-making for users with negligible performance overhead. |
| first_indexed | 2024-03-13T06:38:31Z |
| format | Article |
| id | doaj.art-dbfb811523994bd28c50b4fdac2d9870 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:38:31Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dbfb811523994bd28c50b4fdac2d98702023-06-08T23:00:49ZengIEEEIEEE Access2169-35362023-01-0111556775569110.1109/ACCESS.2023.328092810138384Security-Aware Provenance for Transparency in IoT Data PropagationFariha Tasmin Jaigirdar0https://orcid.org/0000-0003-1119-6056Boyu Tan1Carsten Rudolph2https://orcid.org/0000-0001-9050-5675Chris Bain3Department of Software Systems and Cybersecurity, Faculty of Information Technology, Monash University, Clayton, VIC, AustraliaChina Mobile Group Design Institute Company Ltd., Beijing, ChinaDepartment of Software Systems and Cybersecurity, Faculty of Information Technology, Monash University, Clayton, VIC, AustraliaDepartment of Software Systems and Cybersecurity, Faculty of Information Technology, Monash University, Clayton, VIC, AustraliaA successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of data collected from numerous sources. A significant concern in IoT systems arises when end-users do not have sufficient transparency and are unaware of any potential data manipulation and risk in each step involved in data propagation. One potential solution is to integrate security metadata in IoT-based security-aware provenance graphs that provides better transparency with security awareness at each step of data propagation. In this paper, we integrate security metadata into the provenance graph with predefined security policies. We design a hypothetical IoT-Health scenario with possible threats: node cloning, fault packet injection, denial of service, unauthorized access, and malicious code injection. We simulate these threats in six cases to identify relevant risks. Our findings show how a security-aware provenance graph can offer end users greater transparency and security awareness by identifying failed signature verification (case 1), denial of service (case 2), unauthorized access (case 3), intrusion detection (case 4), missing WAF (case 5), and permission violation (case 6). We evaluate the transparency through obtaining authentication, integrity, availability and detecting underlying threats. Accordingly, this study promotes better risk assessment and decision-making for users with negligible performance overhead.https://ieeexplore.ieee.org/document/10138384/Internet of Things (IoT)data provenanceIoT-Healthtransparencysecurity-awareness |
| spellingShingle | Fariha Tasmin Jaigirdar Boyu Tan Carsten Rudolph Chris Bain Security-Aware Provenance for Transparency in IoT Data Propagation IEEE Access Internet of Things (IoT) data provenance IoT-Health transparency security-awareness |
| title | Security-Aware Provenance for Transparency in IoT Data Propagation |
| title_full | Security-Aware Provenance for Transparency in IoT Data Propagation |
| title_fullStr | Security-Aware Provenance for Transparency in IoT Data Propagation |
| title_full_unstemmed | Security-Aware Provenance for Transparency in IoT Data Propagation |
| title_short | Security-Aware Provenance for Transparency in IoT Data Propagation |
| title_sort | security aware provenance for transparency in iot data propagation |
| topic | Internet of Things (IoT) data provenance IoT-Health transparency security-awareness |
| url | https://ieeexplore.ieee.org/document/10138384/ |
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