petojson
Serialize PE to Json based on PE-Parse (by sebdraven)
ember
Elastic Malware Benchmark for Empowering Researchers (by elastic)
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petojson | ember | |
---|---|---|
1 | 4 | |
8 | 896 | |
- | 2.7% | |
0.0 | 0.0 | |
over 6 years ago | 7 months ago | |
Python | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
petojson
Posts with mentions or reviews of petojson.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-22.
-
Malware & deep learning
Basic features aren't that hard, if all you care about is 90-95% accuracy. You can start with pefile (https://pypi.org/project/pefile/). I forget what exactly I did -- but I dumped everything to JSON. I might have built the dict myself or maybe there was an easy way. There's this, which might be useful: https://github.com/sebdraven/petojson
ember
Posts with mentions or reviews of ember.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-21.
-
[D] Malware Detection Analysis Using Machine Learning
Your other option is the EMBER dataset, which has pre-vectorized feature vectors available to use. Unfortunately, that is also quite limiting: there isn't much you can do with the vectorized data that hasn't already been done. But it would let you work with something much bigger scale for free.
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[Suggestions] Malware Detection Analysis Using Machine Learning
Check out ember: https://github.com/elastic/ember
- Large Benign “goodware” Samples
- Malware & deep learning
What are some alternatives?
When comparing petojson and ember you can also consider the following projects:
SOREL-20M - Sophos-ReversingLabs 20 million sample dataset
MalConv-keras - This is the implementation of MalConv proposed in [Malware Detection by Eating a Whole EXE](https://arxiv.org/abs/1710.09435) and its adversarial sample crafting.
Angular - Deliver web apps with confidence 🚀