Student Work

Predictive Analysis

公开

可下载的内容

open in viewer

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
Publisher
Identifier
  • E-project-101219-164846
Advisor
Year
  • 2019
Center
Sponsor
Date created
  • 2019-10-12
Resource type
Major
Rights statement

关系

属于 Collection:

项目

单件

Permanent link to this page: https://digital.wpi.edu/show/3484zk67g