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Identifying EOL software

Neal D.

Throughout our project, we have been exploring ways to identify EOL and malicious websites using python. Creating a new variable with the keywords, we were able to correctly predict 99% of the EOL websites using the data given to us.

Throughout our project, we have been exploring ways to identify EOL and malicious websites using python. With this software, we can help people stay away from these types of websites. There are lots of cybersecurity problems in the world, but we wanted to address the EOL problem because it is something that is within the realm of what we are learning (language recognition), and it has a practical use in the world. We used multiple different commands to identify certain “keywords,” with the intention of getting the highest possible accuracy percentage. We also prioritized the elimination of false negatives over false positives. Creating a new variable with the keywords, we were able to correctly predict 99% of the EOL websites using the data given to us. We were able to conclude that mixing the 2 most important data columns and creating one variable to determine both of the variables is the best way to go about creating a variable that gives you the most.

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Neal D.
Clayton Greenberg
Teaching Professor from UPenn

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