MSSA:用于二进制代码相似性检测的多阶段语义意识神经网络
Bangrui Wan1,2, Jianjun Zhou1, Ying Wang1
1School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.
PeerJ. Computer science
|February 3, 2025
概括
这项研究介绍了MSSA,一种用于二进制代码相似性检测的轻量级神经网络. MSSA有效地识别了类似的代码函数,在分类任务中表现优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 网络安全 网络安全
背景情况:
- 二进制代码相似性检测 (BCSD) 对于恶意软件分析,补丁分析和克隆检测至关重要.
- 对于BCSD,现有的基于变压器的方法需要大量的计算资源.
- 目前基于学习的方法在捕捉深度二进制代码语义方面存在局限性.
研究的目的:
- 提出MSSA,一个新的多阶段语义意识神经网络,用于功能级BCSD.
- 开发一个适合CPU环境的轻量级模型.
- 为了提高对二进制代码深度语义的理解.
主要方法:
- MSSA集成了组装指令的语义和结构信息.
- 该模型利用四个语义意识的神经网络进行全面分析.
- 它在基本块内和基础块之间以及整个功能之间处理信息.
主要成果:
- 与双子座,Asm2Vec,SAFE和jTrans相比,MSSA表现出优越的分类性能.
- 在检索性能方面,MSSA仅次于基于变压器的jTrans.
- 拟议的模型是轻量级的,其骨干网络中只有0.38M的参数.
结论:
- MSSA为二进制代码相似性检测提供了有效和高效的解决方案.
- 该模型的轻量级性质使其适合实际部署.
- 通过对二进制代码进行更深入的语义理解,MSSA在该领域取得了进展.
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