Walid J. Ghandour, Clémentine Maurice (CNRS, CRIStAL)

Dynamic dependence analysis monitors information flow between instructions in a program at runtime. Strength-based dynamic dependence analysis quantifies the strength of each dependence chain by a measure computed based on the values induced at the source and target of the chain. To the best of our knowledge, there is currently no tool available that implements strength-based dynamic information flow analysis for x86.

This paper presents DITTANY, tool support for strength-based dynamic dependence analysis and experimental evidence of its effectiveness on the x86 platform. It involves two main components: 1) a Pin-based profiler that identifies dynamic dependences in a binary executable and records the associated values induced at their sources and targets, and 2) an analysis tool that computes the strengths of the identified dependences using information theoretic and statistical metrics applied on their associated values. We also study the relation between dynamic dependences and measurable information flow, and the usage of zero strength flows to enhance performance.

DITTANY is a building block that can be used in different contexts. We show its usage in data value and indirect branch predictions. Future work will use it in countermeasures against transient execution attacks and in the context of approximate computing.

View More Papers

FakeGuard: Exploring Haptic Response to Mitigate the Vulnerability in...

Aditya Singh Rathore (University at Buffalo, SUNY), Yijie Shen (Zhejiang University), Chenhan Xu (University at Buffalo, SUNY), Jacob Snyderman (University at Buffalo, SUNY), Jinsong Han (Zhejiang University), Fan Zhang (Zhejiang University), Zhengxiong Li (University of Colorado Denver), Feng Lin (Zhejiang University), Wenyao Xu (University at Buffalo, SUNY), Kui Ren (Zhejiang University)

Read More

P4DDPI: Securing P4-Programmable Data Plane Networks via DNS Deep...

Ali AlSabeh (University of South Carolina), Elie Kfoury (University of South Carolina), Jorge Crichigno (University of South Carolina) and Elias Bou-Harb (University of Texas at San Antonio)

Read More

Demo #5: Disclosing the Pringles Syndrome in Tesla FSD...

Zhisheng Hu (Baidu), Shengjian Guo (Baidu) and Kang Li (Baidu)

Read More