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Pluvio system uses transfer learning for out-of-domain assembly clone search

Researchers have developed Pluvio, a novel system for identifying cloned assembly code across different architectures and libraries. This approach utilizes transfer learning from large-scale pre-trained natural language models to incorporate human expertise into assembly code analysis. Pluvio also employs a reinforcement learning agent to refine token sequences and a Variational Information Bottleneck strategy to enhance generalization to unseen architectures and optimization settings. AI

IMPACT Introduces a new method for code reuse analysis that could improve software security and intellectual property protection.

RANK_REASON This is a research paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Pluvio system uses transfer learning for out-of-domain assembly clone search

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This is a research paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhiwei Fu, Steven H. H. Ding, Furkan Alaca, Benjamin C. M. Fung, Philippe Charland ·

    Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck

    arXiv:2307.10631v2 Announce Type: replace-cross Abstract: The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle. In reality, however, code reuse practices lack proper control, resulting in issues such as vulnerability …