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Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities.
Chen, Kejun; Guo, Xiaolong; Deng, Qingxu; Jin, Yier.
Afiliação
  • Chen K; School of Computer Science and Engineering, Northeastern University, Shenyang 110016, China.
  • Guo X; Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA.
  • Deng Q; School of Computer Science and Engineering, Northeastern University, Shenyang 110016, China.
  • Jin Y; Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA.
Micromachines (Basel) ; 12(8)2021 Jul 29.
Article em En | MEDLINE | ID: mdl-34442520
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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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article