Learning from Context: Exploiting and Interpreting File Path Information for Better Malware Detection

TL;DR

This paper introduces a multi-view neural network that leverages file path context alongside PE file content to improve malware detection accuracy, demonstrating significant false negative rate reductions on a large real-world dataset.

Contribution

The study proposes a novel multi-view neural network model that incorporates file path information into static malware detection, enhancing detection performance with minimal additional infrastructure.

Findings

32.3% reduction in false negatives at 10^-3 FPR

33.1% reduction in false negatives at 10^-4 FPR

Model learns meaningful file path features and artifacts

Abstract

Machine learning (ML) used for static portable executable (PE) malware detection typically employs per-file numerical feature vector representations as input with one or more target labels during training. However, there is much orthogonal information that can be gleaned from the \textit{context} in which the file was seen. In this paper, we propose utilizing a static source of contextual information -- the path of the PE file -- as an auxiliary input to the classifier. While file paths are not malicious or benign in and of themselves, they do provide valuable context for a malicious/benign determination. Unlike dynamic contextual information, file paths are available with little overhead and can seamlessly be integrated into a multi-view static ML detector, yielding higher detection rates at very high throughput with minimal infrastructural changes. Here we propose a multi-view neural network, which takes feature vectors from PE file content as well as corresponding file paths as inputs and outputs a detection score. To ensure realistic evaluation, we use a dataset of approximately 10 million samples -- files and file paths from user endpoints of an actual security vendor network. We then conduct an interpretability analysis via LIME modeling to ensure that our classifier has learned a sensible representation and see which parts of the file path most contributed to change in the classifier's score. We find that our model learns useful aspects of the file path for classification, while also learning artifacts from customers testing the vendor's product, e.g., by downloading a directory of malware samples each named as their hash. We prune these artifacts from our test dataset and demonstrate reductions in false negative rate of 32.3% at a $10^{-3}$ false positive rate (FPR) and 33.1% at $10^{-4}$ FPR, over a similar topology single input PE file content only model.