Visualization Of Binary Features In Large Malware Corpora
MalGrid is a novel method to visualize and map millions of malware to points in a 2-dimensional (2D) spatial grid. This enables visualizing relationships within large malware datasets that can be used to develop triage solutions to screen different malware rapidly and provide situational awareness. Our first view is a spatial point-based visualization of similarity among the samples based on a reduced dimensional projection of binary feature representations of malware. Our second spatial grid-based view provides a better insight into similarities and differences between selected malware samples in terms of the binary-based visual representations they share.
+ HIGH-QUALITY IMAGES IN THE PAPER ARE AVAILABLE
- CODE IS NOT MADE AVAILABLE AT THIS TIMEIf you use MalGrid in your research or wish to refer to the content published here, please use the following BibTeX entry to cite our paper, MalGrid: Visualization Of Binary Features In Large Malware Corpora (2022):
@inproceedings{mohammedmalgrid,
title={MalGrid: Visualization Of Binary Features In Large Malware Corpora},
author={Mohammed, Tajuddin Manhar and Nataraj, Lakshmanan and Chikkagoudar, Satish and Chandrasekaran, Shivkumar and Manjunath, BS},
booktitle={MILCOM 2022-2022 IEEE Military Communications Conference (MILCOM)},
organization={IEEE}
}Please feel free to also check out our other related published works:
- SPAM: Signal Processing to Analyze Malware (2016)
- OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features (2021)
- HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis (2021)
- Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning (2021)
Also, check out our web-accessible service, MalSee that recasts suspect software binaries as images and exploits computer vision techniques to automatically detect malware.
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