ember | MOTIF | |
---|---|---|
4 | 4 | |
904 | 137 | |
1.9% | 1.5% | |
0.0 | 0.0 | |
8 months ago | over 1 year ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | 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.
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.
-
[Suggestions] Malware Detection Analysis Using Machine Learning
Check out ember: https://github.com/elastic/ember
- Large Benign “goodware” Samples
- Malware & deep learning
MOTIF
Posts with mentions or reviews of MOTIF.
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
The easiest place to start, in my super humble opinion, is the MOTIF dataset - which is "small" in the total number of samples but is one of the only high-quality labeled malware family datasets. Then you can look at a number of problems related to malware family detection/classification, on something small enough that you can work within school project resources, and have really real labels and quality.
- GitHub - boozallen/MOTIF - The Malware Open-source Threat Intelligence Family (MOTIF) dataset contains 3,095 disarmed PE malware samples from 454 families, labeled with ground truth confidence
- GitHub - Malware Open-source Threat Intelligence Family (MOTIF) dataset from Booz Allen Hamilton contains 3,095 disarmed PE malware samples from 454 families, labeled with ground truth confidence.
What are some alternatives?
When comparing ember and MOTIF 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 🚀