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

CBAT: A Comparative Binary Analysis Tool

Chloe Fortuna (STR), JT Paasch (STR), Sam Lasser (Draper), Philip Zucker (Draper), Chris Casinghino (Jane Street), Cody Roux (AWS)

Read More

Too Afraid to Drive: Systematic Discovery of Semantic DoS...

Ziwen Wan (University of California, Irvine), Junjie Shen (University of California, Irvine), Jalen Chuang (University of California, Irvine), Xin Xia (The University of California, Los Angeles), Joshua Garcia (University of California, Irvine), Jiaqi Ma (The University of California, Los Angeles), Qi Alfred Chen (University of California, Irvine)

Read More

Demo #14: In-Vehicle Communication Using Named Data Networking

Zachariah Threet (Tennessee Tech), Christos Papadopoulos (University of Memphis), Proyash Poddar (Florida International University), Alex Afanasyev (Florida International University), William Lambert (Tennessee Tech), Haley Burnell (Tennessee Tech), Sheikh Ghafoor (Tennessee Tech) and Susmit Shannigrahi (Tennessee Tech)

Read More

Above and Beyond: Organizational Efforts to Complement U.S. Digital...

Rock Stevens (University of Maryland), Faris Bugra Kokulu (Arizona State University), Adam Doupé (Arizona State University), Michelle L. Mazurek (University of Maryland)

Read More