CSGraph2Vec: Distributed Graph-Based Representation Learning for Assembly Functions

Software reverse engineering is an essential but time-consuming undertaking in identifying malware, software vulnerabilities, and plagiarism, especially when access to the source code is limited. The extraction of abstract characteristics that represent malware and work as classifier inputs in traditional machine learning approaches requires feature engineering. The calibre of the features that are extracted has a major impact on how well these algorithms perform. In contrast, end-to-end learning solutions do not require hand-designed features and instead attempt to determine if an executable is harmful or not. However, a certain level of preprocessing remains essential in order to present malware content in a manner that the machine learning algorithm can comprehend. Due to the development of machine learning and deep learning, automating the construction of vector embeddings has become more feasible. This research introduces CSGraph2Vec, a distributed and automated deep learning approach that produces representations of assembly functions. Using the power of the Electra pre-trained language model, as well as message-passing neural networks, CSGraph2Vec efficiently incorporates control flow and semantic information from assembly code. Our model successfully learns significant features that distinguish benign from malicious functions. Through extensive experimentation and evaluation of the malware classification task, we show that our model performs better than several alternative approaches.