malware-classification | Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector M | Machine Learning library
kandi X-RAY | malware-classification Summary
kandi X-RAY | malware-classification Summary
malware-classification is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. malware-classification has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub, GitLab.
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine for Malware Classification
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine for Malware Classification
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Quality
Security
License
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Support
malware-classification has a low active ecosystem.
It has 150 star(s) with 78 fork(s). There are 11 watchers for this library.
It had no major release in the last 12 months.
There are 0 open issues and 10 have been closed. On average issues are closed in 75 days. There are 3 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of malware-classification is v0.1-alpha
Quality
malware-classification has 0 bugs and 0 code smells.
Security
malware-classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
malware-classification code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
malware-classification is licensed under the GPL-3.0 License. This license is Strong Copyleft.
Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
Reuse
malware-classification releases are available to install and integrate.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
malware-classification saves you 255 person hours of effort in developing the same functionality from scratch.
It has 619 lines of code, 27 functions and 8 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed malware-classification and discovered the below as its top functions. This is intended to give you an instant insight into malware-classification implemented functionality, and help decide if they suit your requirements.
- Train the model
- Save the predicted and actual labels
- Plot the confusion matrix
- List all files in path
- Predict for a given model
- Parse command line arguments
- Load features and labels
- Encode labels
Get all kandi verified functions for this library.
malware-classification Key Features
No Key Features are available at this moment for malware-classification.
malware-classification Examples and Code Snippets
No Code Snippets are available at this moment for malware-classification.
Community Discussions
Trending Discussions on malware-classification
QUESTION
Visualizing executable byte stream to a image file, why it is rotated 45 degrees?
Asked 2019-Feb-21 at 14:53
I'm trying to visualize malware executables for testing visual classification approach. Using Microsoft Malware Classification Challange dataset .bytes files I have input such:
...ANSWER
Answered 2019-Feb-21 at 14:53I think the line:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install malware-classification
You can download it from GitHub, GitLab.
You can use malware-classification 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 malware-classification 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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