Visible to the public Representation Learning with Function Call Graph Transformations for Malware Open Set Recognition

TitleRepresentation Learning with Function Call Graph Transformations for Malware Open Set Recognition
Publication TypeConference Paper
Year of Publication2022
AuthorsJia, Jingyun, Chan, Philip K.
Conference Name2022 International Joint Conference on Neural Networks (IJCNN)
Date Publishedjul
KeywordsCosts, graph theory, Human Behavior, Malware, malware analysis, malware classification, Metrics, Neural networks, open set recognition, privacy, pubcrawl, representation learning, resilience, Resiliency, Resiliency Coordinator, security, self-supervised learning, Task Analysis, Training
AbstractOpen set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training process in ML systems. An advanced malware classification system should classify the known classes correctly while sensitive to the unknown class. In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification. We propose two transformations for the function call graph (FCG) based malware representations to facilitate the pretext task. Also, we present a statistical thresholding approach to find the optimal threshold for the unknown class. Moreover, the experiment results indicate that our proposed pre-training process can improve different performances of different downstream loss functions for the OSR problem.
Citation Keyjia_representation_2022