Identifying EOL Software


  • Neal Damireddy Foothill High School
  • Clayton Greenberg



Obsolete Software, machine learning, natural language processing


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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References or Bibliography

Takata, Yuta, et al. "Identifying evasive code in malicious websites by analyzing redirection differences." IEICE Transactions on Information and Systems 101.11 (2018): 2600-2611.

Khan, Hafiz Mohammd Junaid, et al. "Identifying generic features for malicious url detection system." 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2019.



How to Cite

Damireddy, N., & Greenberg, C. . (2023). Identifying EOL Software. Journal of Student Research, 12(3).



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