论文标题
使用离心机机制用于多CPU架构的编译器出处恢复
Compiler Provenance Recovery for Multi-CPU Architectures Using a Centrifuge Mechanism
论文作者
论文摘要
位流识别（BSR）具有许多应用，例如法医研究，侵犯版权的检测和恶意软件分析。我们提出了第一个BSR，该BSR采用裸露的输入位流并输出类标签，而无需进行任何预处理。为了实现我们的目标，我们提出了一个离心机机制，即即使输入位点的一部分具有相同的值，上游层（子网）捕获全局特征并告诉下游层（主网）以切换焦点。我们将离心机机制应用于编译器出处恢复（一种BSR），并实现了出色的分类。此外，下游转移学习（DTL）是我们针对离心机机制提出的一种学习方法之一，它使用子网基的地面真实而不是子网络的输出进行了主网训练。我们发现，当子标签分类有助于主要预测的本质时，DTL提出的子预测往往非常准确。
Bit-stream recognition (BSR) has many applications, such as forensic investigations, detection of copyright infringement, and malware analysis. We propose the first BSR that takes a bare input bit-stream and outputs a class label without any preprocessing. To achieve our goal, we propose a centrifuge mechanism, where the upstream layers (sub-net) capture global features and tell the downstream layers (main-net) to switch the focus, even if a part of the input bit-stream has the same value. We applied the centrifuge mechanism to compiler provenance recovery, a type of BSR, and achieved excellent classification. Additionally, downstream transfer learning (DTL), one of the learning methods we propose for the centrifuge mechanism, pre-trains the main-net using the sub-net's ground truth instead of the sub-net's output. We found that sub-predictions made by DTL tend to be highly accurate when the sub-label classification contributes to the essence of the main prediction.
