SIGL: Securing Software Installations Through Deep Graph Learning

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.

Deep Program Reidentification: A Graph Neural Network Solution

Program or process is an integral part of almost every IT/OT system. Can we trust the identity/ID (e.g., executable name) of the program? To avoid detection, malware may disguise itself using the ID of a legitimate program, and a system tool (e.g., PowerShell) used by the attackers may have the fake ID of another common software, which is less sensitive. However, existing intrusion detection techniques often overlook this critical program reidentification problem (i.e., checking the program's identity). In this paper, we propose an attentional multi-channel graph neural network model (DeepRe-ID) to verify the program's identity based on its system behaviors. The key idea is to leverage the representation learning of the program behavior graph to guide the reidentification process. We formulate the program reidentification as a graph classification problem and develop an effective multi-channel attentional graph embedding algorithm to solve it. Extensive experiments --- using real-world enterprise monitoring data and real attacks --- demonstrate the effectiveness of DeepRe-ID across multiple popular metrics and the robustness to the normal dynamic changes like program version upgrades.