SIGL：通过深度图学习保护软件安装
SIGL: Securing Software Installations Through Deep Graph Learning
摘要：
许多用户默认软件只有在安装后才能被利用。然而，最近的供应链攻击表明，必须在安装过程中确保应用程序的完整性。我们介绍了SIGL，一个新的工具，用于检测软件安装过程中的恶意行为。SIGL收集系统调用活动的痕迹，构建数据起源图，使用一种新型的自动编码器架构进行分析，该架构具有用于编码器的图长短期记忆网络（图LSTM）和用于解码器的标准多层感知器。SIGL标记可疑安装以及可能是恶意的特定安装时进程。使用包含真实恶意软件的625个恶意安装程序的测试语料库，我们证明了SIGL的检测准确率为96%，在精确度和召回率方面优于工业界和学术界的类似系统高达87%，准确率为45%。我们还证明了SIGL可以精确定位最有可能触发恶意行为的进程，可以在不同的审计平台和操作系统上工作，并且对训练数据污染和对抗攻击具有鲁棒性。它可以与特定于应用程序的模型一起使用，即使在存在新软件版本的情况下，也可以与包含广泛应用程序和安装程序的应用程序无关的元模型一起使用。
英文摘要：
Many users implicitly assume that software can only be exploited after it is installed. However, recent supply-chain attacks demonstrate that application integrity must be ensured during installation itself. We introduce SIGL, a new tool for detecting malicious behavior during software installation. SIGL collects traces of system call activity, building a data provenance graph that it analyzes using a novel autoencoder architecture with a graph long short-term memory network (graph LSTM) for the encoder and a standard multilayer perceptron for the decoder. SIGL flags suspicious installations as well as the specific installation-time processes that are likely to be malicious. Using a test corpus of 625 malicious installers containing real-world malware, we demonstrate that SIGL has a detection accuracy of 96%, outperforming similar systems from industry and academia by up to 87% in precision and recall and 45% in accuracy. We also demonstrate that SIGL can pinpoint the processes most likely to have triggered malicious behavior, works on different audit platforms and operating systems, and is robust to training data contamination and adversarial attack. It can be used with application-specific models, even in the presence of new software versions, as well as application-agnostic meta-models that encompass a wide range of applications and installers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
