The rise of cybercrime has motivated the need for improved early detection and prediction mechanisms to prevent cyber-attacks from causing damage to unsuspecting victims. We developed and analyzed various machine learning algorithms to tackle one approach for early detection, URL classification. Unlike previous research, which focused on binary classification, our approach focuses on classifying URLs to their likely attack category. Through testing and evaluation, we found that ensemble methods perform the best with our optimal feature set, producing accuracies as high as 95%.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Tavares, Alex Michael
• Dwan, Robert Alan

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-101219-164846

Advisor

• Heineman, George T.

Year

• 2019

Center

• MIT Lincoln Laboratory-Lexington, Massachusetts Project Center - MQP

Sponsor

• MIT Lincoln Laboratory

Date created

• 2019-10-12

Resource type

• Major Qualifying Project

Major

• Computer Science

Rights statement

• In Copyright

License

• All rights reserved