Kimberly Redmond (University of South Carolina), Lannan Luo (University of South Carolina), Qiang Zeng (University of South Carolina)

Given a closed-source program, such as most of proprietary software and viruses, binary code analysis is indispensable for many tasks, such as code plagiarism detection and malware analysis. Today, source code is very often compiled for various architectures, making cross-architecture binary code analysis increasingly important. A binary, after being disassembled, is expressed in an assembly language. Thus, recent work starts exploring Natural Language Processing (NLP) inspired binary code analysis. In NLP, words are usually represented in high-dimensional vectors (i.e., embeddings) to facilitate further processing, which is one of the most common and critical steps in many NLP tasks. We regard instructions as words in NLP-inspired binary code analysis, and aim to represent instructions as embeddings as well.

To facilitate cross-architecture binary code analysis, our goal is that similar instructions, regardless of their architectures, have embeddings close to each other. To this end, we propose a joint learning approach to generating instruction embeddings that capture not only the semantics of instructions within an architecture, but also their semantic relationships across architectures. To the best of our knowledge, this is the first work on building cross-architecture instruction embedding model. As a showcase, we apply the model to resolving one of the most fundamental problems for binary code similarity comparison—semantics-based basic block comparison, and the solution outperforms the code statistics based approach. It demonstrates that it is promising to apply the model to other cross-architecture binary code analysis tasks.

View More Papers

Similarity Metric Method for Binary Basic Blocks of Cross-Instruction...

Xiaochuan Zhang (Artificial Intelligence Research Center, National Innovation Institute of Defense Technology), Wenjie Sun (State Key Laboratory of Mathematical Engineering and Advanced Computing), Jianmin Pang (State Key Laboratory of Mathematical Engineering and Advanced Computing), Fudong Liu (State Key Laboratory of Mathematical Engineering and Advanced Computing), Zhen Ma (State Key Laboratory of Mathematical Engineering and Advanced…

Read More

FirmLine: a Generic Pipeline for Large-Scale Analysis of Non-Linux...

Alexander Balgavy (Independent), Marius Muench (University of Birmingham)

Read More

Performance, Correctness, Exceptions: Pick Three

Andrea Gussoni (Politecnico di Milano), Alessandro Di Federico (Politecnico di Milano), Pietro Fezzardi (Politecnico di Milano), Giovanni Agosta (Politecnico di Milano)

Read More

Accurate Compiler and Optimization Independent Function Identification Using Program...

Derrick McKee (Purdue University), Nathan Burow (MIT Lincoln Laboratory), Mathias Payer (EPFL)

Read More