Static analysis, a cornerstone technique in cybersecurity, offers a noninvasive method for detecting malware by analyzing dormant software without executing potentially harmful code. However, traditional static analysis often relies on biased or outdated datasets, leading to gaps in detection capabilities against emerging malware threats. To address this, our study focuses on the binary content of files as key features for malware detection. These binary contents are transformed and represented as images, which then serve as inputs to deep learning models. This method takes into account the visual patterns within the binary data, allowing the model to analyze potential malware effectively. This paper introduces the application of the CBiGAN in the domain of malware anomaly detection. Our approach leverages the CBiGAN for its superior latent space mapping capabilities, critical for modeling complex malware patterns by utilizing a reconstruction error-based anomaly detection method. We utilized several datasets including both portable executable (PE) files as well as Object Linking and Embedding (OLE) files. We then evaluated our model against a diverse set of both PE and OLE files, including self-collected malicious executables from 214 malware families. Our findings demonstrate the robustness of this innovative approach, with the CBiGAN achieving high Area Under the Curve (AUC) results with good generalizability, thereby confirming its capability to distinguish between benign and diverse malicious files with reasonably high accuracy.
View on arXiv@article{wijayasiri2025_2506.07372,
title={ Enhanced Consistency Bi-directional GAN(CBiGAN) for Malware Anomaly Detection },
author={ Thesath Wijayasiri and Kar Wai Fok and Vrizlynn L. L. Thing },
journal={arXiv preprint arXiv:2506.07372},
year={ 2025 }
}