Declarative Demand-Driven Reverse Engineering
Yihao Sun, Jeffrey Ching, Kristopher Micinski

TL;DR
This paper proposes a declarative, demand-driven approach to binary reverse engineering that integrates visualization tools with logical inference engines, improving efficiency and expressiveness in analyzing binaries.
Contribution
It introduces the D^3RE framework, formal semantics, and a prototype tool that enhances reverse engineering by combining visualization with logical inference.
Findings
D^3RE improves performance in binary analysis tasks.
D^3RE enables more succinct implementation of reverse engineering queries.
Prototype successfully reimplements common RE tasks.
Abstract
Binary reverse engineering is a challenging task because it often necessitates reasoning using both domain-specific knowledge (e.g., understanding entrypoint idioms common to an ABI) and logical inference (e.g., reconstructing interprocedural control flow). To help perform these tasks, reverse engineers often use toolkits (such as IDA Pro or Ghidra) that allow them to interactively explicate properties of binaries. We argue that deductive databases serve as a natural abstraction for interfacing between visualization-based binary analysis tools and high-performance logical inference engines that compute facts about binaries. In this paper, we present a vision for the future in which reverse engineers use a visualization-based tool to understand binaries while simultaneously querying a logical-inference engine to perform arbitrarily-complex deductive inference tasks. We call our vision…
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Taxonomy
TopicsSemantic Web and Ontologies · Software Engineering Research · Advanced Database Systems and Queries
