SAD THUG: Structural Anomaly Detection for Transmissions of High-value Information Using Graphics

Authors: 

Jonathan P. Chapman, Fraunhofer FKIE

Abstract: 

The use of hidden communication methods by malware families skyrocketed in the last two years. Ransomware like Locky, Cerber or CryLocker, but also banking trojans like Zberp or ZeusVM, use image files to hide their tracks. Additionally, malware employed for targeted attacks has been using similar techniques for many years. The DuQu and Hammertoss families, for instance, use the popular JPEG file format to clandestinely exchange messages. Using these techniques, they easily bypass systems designed to protect sensitive networks against them. In this paper, we show that these methods result in structural changes to the respective files. Thus, infections with these malware families can be detected by identifying image files with an unusual structure. We developed a structural anomaly detection approach that is based on this insight. In our evaluation, SAD THUG achieves a mean true positive ratio of 99.24% for JPEG files using 10 different embedding methods while maintaining a mean true negative ratio of 99.323%. For PNG files, the latter number drops slightly to 98.88% but the mean true positive ratio improves to 99.318%. We only rely on the fact that these methods change the structure of their cover file. Thus, as we show in this paper, our approach is not limited to detecting a particular set of malware information hiding methods but can detect virtually any method that changes the structure of a container file.

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BibTeX
@inproceedings {217533,
author = {Jonathan P. Chapman},
title = {{SAD} {THUG}: Structural Anomaly Detection for Transmissions of High-value Information Using Graphics},
booktitle = {27th {USENIX} Security Symposium ({USENIX} Security 18)},
year = {2018},
isbn = {978-1-931971-46-1},
address = {Baltimore, MD},
pages = {1147--1164},
url = {https://www.usenix.org/conference/usenixsecurity18/presentation/chapman},
publisher = {{USENIX} Association},
}