A Natural Language Processing Approach for Instruction Set Architecture Identification

TL;DR

This paper introduces an NLP-inspired binary feature extraction method for machine learning-based identification of instruction set architectures, significantly improving accuracy and efficiency in binary analysis tasks.

Contribution

It proposes a novel character-level binary feature extraction model that enhances ISA identification accuracy and reduces feature set size without requiring domain knowledge.

Findings

8% higher accuracy than state-of-the-art methods

Character-level features reduce feature set size by up to 16x

Accuracy remains above 97% with reduced features

Abstract

Binary analysis of software is a critical step in cyber forensics applications such as program vulnerability assessment and malware detection. This involves interpreting instructions executed by software and often necessitates converting the software's binary file data to assembly language. The conversion process requires information about the binary file's target instruction set architecture (ISA). However, ISA information might not be included in binary files due to compilation errors, partial downloads, or adversarial corruption of file metadata. Machine learning (ML) is a promising methodology that can be used to identify the target ISA using binary data in the object code section of binary files. In this paper we propose a binary code feature extraction model to improve the accuracy and scalability of ML-based ISA identification methods. Our feature extraction model can be used in the absence of domain knowledge about the ISAs. Specifically, we adapt models from natural language processing (NLP) to i) identify successive byte patterns commonly observed in binary codes, ii) estimate the significance of each byte pattern to a binary file, and iii) estimate the relevance of each byte pattern in distinguishing between ISAs. We introduce character-level features of encoded binaries to identify fine-grained bit patterns inherent to each ISA. We use a dataset with binaries from 12 different ISAs to evaluate our approach. Empirical evaluations show that using our byte-level features in ML-based ISA identification results in an 8% higher accuracy than the state-of-the-art features based on byte-histograms and byte pattern signatures. We observe that character-level features allow reducing the size of the feature set by up to 16x while maintaining accuracy above 97%.