FastBCSD: Fast and Efficient Neural Network for Binary Code Similarity Detection

TL;DR

FastBCSD is a lightweight neural network for binary code similarity detection that achieves comparable or better accuracy than larger models while significantly reducing computational costs and inference time.

Contribution

The paper introduces FastBCSD, a neural network that uses dynamic instruction encoding and assembly code input to improve efficiency without sacrificing accuracy.

Findings

Achieves similar or better accuracy than state-of-the-art models.

Reduces parameter size by over 6 times.

Speeds up inference time to one-fifth of previous methods.

Abstract

Binary code similarity detection (BCSD) has various applications, including but not limited to vulnerability detection, plagiarism detection, and malware detection. Previous research efforts mainly focus on transforming binary code to assembly code strings using reverse compilation and then using pre-trained deep learning models with large parameters to obtain feature representation vector of binary code. While these models have proven to be effective in representing binary code, their large parameter size leads to considerable computational expenses during both training and inference. In this paper, we present a lightweight neural network, called FastBCSD, that employs a dynamic instruction vector encoding method and takes only assembly code as input feature to achieve comparable accuracy to the pre-training models while reducing the computational resources and time cost. On the BinaryCorp dataset, our method achieves a similar average MRR score to the state-of-the-art pre-training-based method (jTrans), while on the BinaryCorp 3M dataset, our method even outperforms the latest technology by 0.01. Notably, FastBCSD has a much smaller parameter size (13.4M) compared to jTrans (87.88M), and its latency time is 1/5 of jTrans on NVIDIA GTX 1080Ti.