Papers by Lannan Luo

3 papers
Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures (2024.findings-naacl)

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Challenge: Applying deep learning to binary code analysis has drawn great attention because of its notable performance.
Approach: They propose to learn cross-architecture instruction embeddings where semantically-similar instructions have close embeddements in a shared space.
Outcome: The proposed approach generates high-quality CAIE with good transferability on four ISAs.
FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture (2025.findings-emnlp)

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Challenge: Using deep learning to detect malware has attracted great attention due to its notable performance.
Approach: a new approach uses Neural Machine Translation and Normalizing Flows to apply deep learning to malware detection.
Outcome: The proposed approach reduces the burden of data collection by enabling malware detection across multiple ISAs.
Unsupervised Binary Code Translation with Application to Code Clone Detection and Vulnerability Discovery (2023.findings-emnlp)

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Challenge: a recent study shows that binary code analysis is a key problem in software security research.
Approach: They propose to apply Neural Machine Translation to binary code analysis . they translate a binary in a low-resource ISA and train a model on the high-resourced ISA .
Outcome: The proposed model can be used to analyze binary code across ISAs using a high-resource ISA.

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