easyesn
Python library for Reservoir Computing using Echo State Networks (by kalekiu)
DeepMalwareDetector
A Deep Learning framework that analyses Windows PE files to detect malicious Softwares. (by islem-esi)
| easyesn | DeepMalwareDetector | |
|---|---|---|
| 1 | 1 | |
| 131 | 65 | |
| 0.0% | - | |
| 2.6 | 0.0 | |
| over 3 years ago | about 1 year ago | |
| Python | Python | |
| - | - |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
easyesn
Posts with mentions or reviews of easyesn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-22.
DeepMalwareDetector
Posts with mentions or reviews of DeepMalwareDetector.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-26.
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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?
When comparing easyesn and DeepMalwareDetector you can also consider the following projects:
wtte-rnn - WTTE-RNN a framework for churn and time to event prediction
avclass - AVClass malware labeling tool
stock-prediction-deep-neural-learning - Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting
deepNOID - deepNOID, the binary music genre classifier which determines if what you're listening to really is NOIDED
RNN-Twitter-Bot - 🤖✏️ A Twitter bot written in Python trained with a recurrent neural network.
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