kervolution
DeepMalwareDetector
| kervolution | DeepMalwareDetector | |
|---|---|---|
| 1 | 1 | |
| 38 | 65 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 4 years ago | about 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 only | - |
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kervolution
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Extending kerv2d to kerv3d in https://github.com/wang-chen/kervolution/blob/unfold/kervolution.py
Dear friends, I am trying to create custom kerv3d layer from the existing custom kerv2d layer given in the repository https://github.com/wang-chen/kervolution/blob/unfold/kervolution.py. Can anyone please suggest me suitable modifications to kerv2d layer for getting kerv3d. I want to replace conv3d layer with kerv3d for experimentation. Thank you.
DeepMalwareDetector
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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?
Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
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Unredactor - In this project we are tryinbg to create unredactor. Unredactor will take a redacted document and the redacted flag as input, inreturn it will give the most likely candidates to fill in redacted location. In this project we are only considered about unredacting names only. The data that we are considering is imdb data set with many review files. These files are used to buils corpora for finding tfidf score. Few files are used to train and in these files names are redacted and written into redacted folder. These redacted files are used for testing and different classification models are built to predict the probabilies of each class. Top 5 classes i.e names similar to the test features are written at the end of text in unreddacted foleder.
easyesn - Python library for Reservoir Computing using Echo State Networks
strelka - Real-time, container-based file scanning at enterprise scale
MDML - Malware Detection using Machine Learning (MDML)
MalConv2 - Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection
sharpened-cosine-similarity - An alternative to convolution in neural networks
SparK - [ICLR'23 Spotlightš„] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"