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Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
Dynamic information flow tracking (DIFT) has been proven an effective technique to track data usage; prevent control data attacks and non-control data attacks at runtime; and analyze program performance. Therefore, a series of DIFT techniques have been developed recently. In this paper, we summarize...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399738/ https://www.ncbi.nlm.nih.gov/pubmed/34442520 http://dx.doi.org/10.3390/mi12080898 |
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author | Chen, Kejun Guo, Xiaolong Deng, Qingxu Jin, Yier |
author_facet | Chen, Kejun Guo, Xiaolong Deng, Qingxu Jin, Yier |
author_sort | Chen, Kejun |
collection | PubMed |
description | Dynamic information flow tracking (DIFT) has been proven an effective technique to track data usage; prevent control data attacks and non-control data attacks at runtime; and analyze program performance. Therefore, a series of DIFT techniques have been developed recently. In this paper, we summarize the current DIFT solutions and analyze the features and limitations of these solutions. Based on the analysis, we classify the existing solutions into three categories, i.e., software, hardware, software and hardware co-design. We discuss the DIFT design from the perspective of whole system and point out the limitations of current DIFT frameworks. Potential enhancements to these solutions are also presented. Furthermore, we present suggestions about the possible future direction of DIFT solutions so that DIFT can help improve security levels. |
format | Online Article Text |
id | pubmed-8399738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83997382021-08-29 Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities Chen, Kejun Guo, Xiaolong Deng, Qingxu Jin, Yier Micromachines (Basel) Article Dynamic information flow tracking (DIFT) has been proven an effective technique to track data usage; prevent control data attacks and non-control data attacks at runtime; and analyze program performance. Therefore, a series of DIFT techniques have been developed recently. In this paper, we summarize the current DIFT solutions and analyze the features and limitations of these solutions. Based on the analysis, we classify the existing solutions into three categories, i.e., software, hardware, software and hardware co-design. We discuss the DIFT design from the perspective of whole system and point out the limitations of current DIFT frameworks. Potential enhancements to these solutions are also presented. Furthermore, we present suggestions about the possible future direction of DIFT solutions so that DIFT can help improve security levels. MDPI 2021-07-29 /pmc/articles/PMC8399738/ /pubmed/34442520 http://dx.doi.org/10.3390/mi12080898 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Kejun Guo, Xiaolong Deng, Qingxu Jin, Yier Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities |
title | Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities |
title_full | Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities |
title_fullStr | Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities |
title_full_unstemmed | Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities |
title_short | Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities |
title_sort | dynamic information flow tracking: taxonomy, challenges, and opportunities |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399738/ https://www.ncbi.nlm.nih.gov/pubmed/34442520 http://dx.doi.org/10.3390/mi12080898 |
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