Enriching the Semantics of Information Flow Tracking with Source-Level Memory Allocation Event Logging

Sanoop Mallissery*, Yu Sung Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Information flow tracking (IFT) reveals how a program accesses its data throughout its execution. It can effectively detect the leakage of sensitive data or the corruption of critical data. Much of its strength depends on the semantics of the variables involved. Here, we have devised SQUIRREL, a configurable static code instrumentation and runtime logging tool, which enriches the semantics of information flow representation with detailed source-code level variable mappings. System administrators or intrusion detection systems (IDS) will have precise insight into the information flow, making it possible to detect attacks on zero-day vulnerabilities or application-specific logic loopholes. We evaluate SQUIRREL with various real-world programs and generate information flow with source-level variable mappings and discuss the efficiency of SQUIRREL concerning performance overhead and memory usage with existing profiling tools.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Conference on Dependable and Secure Computing, DSC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350382112
DOIs
StatePublished - 2023
Event6th IEEE Conference on Dependable and Secure Computing, DSC 2023 - Tampa, United States
Duration: 7 Nov 20239 Nov 2023

Publication series

NameProceedings - 2023 IEEE Conference on Dependable and Secure Computing, DSC 2023

Conference

Conference6th IEEE Conference on Dependable and Secure Computing, DSC 2023
Country/TerritoryUnited States
CityTampa
Period7/11/239/11/23

Keywords

  • code analysis
  • code instrumentation
  • dynamic information flow tracking code sanitization

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