https://github.com/jldbc/malicious-urls
Malicious url classifier build with SVM, random forest, and logistic regression classifiers
https://github.com/jldbc/malicious-urls
Last synced: 13 days ago
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Malicious url classifier build with SVM, random forest, and logistic regression classifiers
- Host: GitHub
- URL: https://github.com/jldbc/malicious-urls
- Owner: jldbc
- Created: 2016-07-07T06:13:39.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2016-07-07T06:32:26.000Z (almost 10 years ago)
- Last Synced: 2025-02-23T01:14:04.094Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 1 MB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Malicious Urls: Statistical Learning Project
Malicious url classifier build with SVM, random forest, and logistic regression classifiers.
## Data
The data used is a subset of the UCSD malicious url data set, which can be found [here](http://sysnet.ucsd.edu/projects/url/ "Title").
## Does it Work?
Yes, the classifier is very accurate, correctly classifying approximately 99% of the 56,000 observations tested.
## Is it Practical?
No, not at all. This was solely an academic exercise. Collecting the >3 million features observed in this data set in real time in order to turn this into a real-world security system is far outside the scope of this project.
## How Did you Build it, and How do the Models Work?
Check out [writeup.pdf!](https://github.com/jldbc/malicious-urls/blob/master/writeup.pdf "Title").