Inter-BIN: Interaction-based Cross-architecture IoT Binary Similarity Comparison

TL;DR

Inter-BIN introduces an interaction-based approach with co-attention and lightweight embeddings for cross-architecture IoT binary similarity, effectively improving accuracy and scalability in real-world malware analysis.

Contribution

The paper presents a novel interaction-based method with co-attention and a lightweight embedding scheme for cross-architecture binary similarity comparison, addressing OOV issues and outperforming existing methods.

Findings

Inter-BIN significantly outperforms state-of-the-art approaches.

It is practical and scalable on real-world datasets.

The CrossMal dataset contains 1,878,437 cross-architecture function pairs.

Abstract

The big wave of Internet of Things (IoT) malware reflects the fragility of the current IoT ecosystem. Research has found that IoT malware can spread quickly on devices of different processer architectures, which leads our attention to cross-architecture binary similarity comparison technology. The goal of binary similarity comparison is to determine whether the semantics of two binary snippets is similar. Existing learning-based approaches usually learn the representations of binary code snippets individually and perform similarity matching based on the distance metric, without considering inter-binary semantic interactions. Moreover, they often rely on the large-scale external code corpus for instruction embeddings pre-training, which is heavyweight and easy to suffer the out-of-vocabulary (OOV) problem. In this paper, we propose an interaction-based cross-architecture IoT binary similarity comparison system, Inter-BIN. Our key insight is to introduce interaction between instruction sequences by co-attention mechanism, which can flexibly perform soft alignment of semantically related instructions from different architectures. And we design a lightweight multi-feature fusion-based instruction embedding method, which can avoid the heavy workload and the OOV problem of previous approaches. Extensive experiments show that Inter-BIN can significantly outperform state-of-the-art approaches on cross-architecture binary similarity comparison tasks of different input granularities. Furthermore, we present an IoT malware function matching dataset from real network environments, CrossMal, containing 1,878,437 cross-architecture reuse function pairs. Experimental results on CrossMal prove that Inter-BIN is practical and scalable on real-world binary similarity comparison collections.