Learning Joint Embeddings of Function and Process Call Graphs for Malware Detection

Software systems can be represented as graphs, capturing dependencies among functions and processes. An interesting aspect of software systems is that they can be represented as different types of graphs, depending on the extraction goals and priorities. For example, function calls within the software can be captured to create function call graphs, which highlight the relationships between functions and their dependencies. Alternatively, the processes spawned by the software can be modeled to generate process interaction graphs, which focus on runtime behavior and inter-process communication. While these graph representations are related, each captures a distinct perspective of the system, providing complementary insights into its structure and operation. While previous studies have leveraged graph neural networks (GNNs) to analyze software behaviors, most of this work has focused on a single type of graph representation. The joint modeling of both function call graphs and process interaction graphs remains largely underexplored, leaving opportunities for deeper, multi-perspective analysis of software systems. This paper presents a pipeline for constructing and training \textit{Function Call Graphs (FCGs)} and \textit{Process Call Graphs (PCGs)} and learning joint embeddings. We demonstrate that joint embeddings outperform a single-graph model. In this paper, we propose GeminiNet, a unified neural network approach that learns joint embeddings from both FCGs and PCGs. We construct a new dataset of 635 Windows executables (318 malicious and 317 benign), extracting FCGs via Ghidra and PCGs via Any.Run sandbox. GeminiNet employs dual graph convolutional branches with an adaptive gating mechanism that balances contributions from static and dynamic views. Experiments with five-fold cross-validation demonstrate that GeminiNet achieves a mean F1 score of $0.85$ with a standard deviation of $ 0.06$-$0.09$ for SGC and GCN, and a maximum F1 score of up to $0.94$ for the best configuration. Extensive experiments with multiple graph neural networks demonstrate that GeminiNet's joint embeddings consistently outperform single modalities and the merged-graph modality.