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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...

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Detalles Bibliográficos
Autores principales: Chen, Kejun, Guo, Xiaolong, Deng, Qingxu, Jin, Yier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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