dga_predict | repo contains very simple code | Machine Learning library
kandi X-RAY | dga_predict Summary
kandi X-RAY | dga_predict Summary
dga_predict is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. dga_predict has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However dga_predict build file is not available. You can download it from GitHub.
This repo contains very simple code for classifying domains as DGA or benign. This code demonstrates our results in our arxiv paper here: One difference is the datasets. Both the paper and this repo use the Alexa top 1 million as benign, but this repo generates its own domains for simplicity. We also only implement the LSTM and bigram classifier from the paper. These are the two best classifiers and are simple to implement in Keras.
This repo contains very simple code for classifying domains as DGA or benign. This code demonstrates our results in our arxiv paper here: One difference is the datasets. Both the paper and this repo use the Alexa top 1 million as benign, but this repo generates its own domains for simplicity. We also only implement the LSTM and bigram classifier from the paper. These are the two best classifiers and are simple to implement in Keras.
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Quality
Security
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Support
dga_predict has a low active ecosystem.
It has 244 star(s) with 126 fork(s). There are 18 watchers for this library.
It had no major release in the last 6 months.
There are 5 open issues and 2 have been closed. On average issues are closed in 15 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of dga_predict is current.
Quality
dga_predict has 0 bugs and 0 code smells.
Security
dga_predict has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
dga_predict code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
dga_predict is licensed under the GPL-2.0 License. This license is Strong Copyleft.
Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
Reuse
dga_predict releases are not available. You will need to build from source code and install.
dga_predict has no build file. You will be need to create the build yourself to build the component from source.
dga_predict saves you 284 person hours of effort in developing the same functionality from scratch.
It has 686 lines of code, 46 functions and 17 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed dga_predict and discovered the below as its top functions. This is intended to give you an instant insight into dga_predict implemented functionality, and help decide if they suit your requirements.
- Get data from pickle file
- Generate malicious data
- Generate random domains
- Get the domain names from an ala file
- Generate a list of domains
- Generate next domain
- Map string to lowercase letter
- Generate a domain
- Create and return figures
- Runs the experiments
- Calculate ROC curve
Get all kandi verified functions for this library.
dga_predict Key Features
No Key Features are available at this moment for dga_predict.
dga_predict Examples and Code Snippets
No Code Snippets are available at this moment for dga_predict.
Community Discussions
Trending Discussions on dga_predict
QUESTION
" samples: %r" % [int(l) for l in lengths]) ValueError: Found input variables with inconsistent numbers of samples: [219870, 0, 0]
Asked 2021-Jun-12 at 20:22
I'm trying to train some ML algorithms on some data that I collected, but I received an error for input variables with inconsistent numbers of samples. I'm not really sure what variables needs to be changed or not. I've posted my code below to give you a better understanding of what I'm trying to accomplish:
...ANSWER
Answered 2021-Jun-12 at 12:14The file has to be opened in binary mode.
open(DATA_FILE, 'rb')
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install dga_predict
You can download it from GitHub.
You can use dga_predict like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use dga_predict like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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