strelka
DeepMalwareDetector
strelka | DeepMalwareDetector | |
---|---|---|
1 | 1 | |
803 | 65 | |
1.7% | - | |
9.3 | 0.0 | |
11 days ago | about 1 year ago | |
Python | Python | |
GNU General Public License v3.0 or later | - |
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strelka
DeepMalwareDetector
-
Looking for insight on labelling portable executable (PE) malware files using a VirusTotal API response report.
What brought me to this research was these studies [1] [2], which demonstrates how image-based malware classification can be done using a CNN (convolutional neural network). Since I had a bit of a background with malware, and I recently completed a CNN model, I figured I would try to do something similar. It was only after investigating different materials I hit a bit of a roadblock. I found this one dataset, malimg [3], which is made up of PE files that have been converted into images already. I didn't want to just use the images, I wanted to demonstrate how to get them, only the method used to classify them turned out to be a bit out of my depth, kind of like this whole project, it's discussed in Section 4.2 of this paper [4] . There's also this set [5], which contains the pixel content for each file record. And as for the static disassembly you mention, I think you are right, the training data might not exist. During my investigation the best I could find was this study [6].
What are some alternatives?
uzen - Website crawler with YARA detection
avclass - AVClass malware labeling tool
halogen - Automatically create YARA rules from malicious documents.
deepNOID - deepNOID, the binary music genre classifier which determines if what you're listening to really is NOIDED
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
tinysleepnet - TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG by Akara Supratak and Yike Guo from The Faculty of ICT, Mahidol University and Imperial College London respectively