Clustering Web Users by Mouse Movement to Detect Bots and Botnet Attacks

<p>The need for website administrators to efficiently and accurately detect the presence of web bots has shown to be a challenging problem. As the sophistication of modern web bots increases, specifically their ability to more closely mimic the behavior of humans, web bot detection schemes are more quickly becoming obsolete by failing to maintain effectiveness. Though machine learning-based detection schemes have been a successful approach to recent implementations, web bots are able to apply similar machine learning tactics to mimic human users, thus bypassing such detection schemes. This work seeks to address the issue of machine learning based bots bypassing machine learning-based detection schemes, by introducing a novel unsupervised learning approach to cluster users based on behavioral biometrics. The idea is that, by differentiating users based on their behavior, for example how they use the mouse or type on the keyboard, information can be provided for website administrators to make more informed decisions on declaring if a user is a human or a bot. This approach is similar to how modern websites require users to login before browsing their website; which in doing so, website administrators can make informed decisions on declaring if a user is a human or a bot. An added benefit of this approach is that it is a human observational proof (HOP); meaning that it will not inconvenience the user (user friction) with human interactive proofs (HIP) such as CAPTCHA, or with login requirements</p>

Name thesis:degree_name

MS in Computer Science

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2021

Author dc:creator

• Morgan, Justin L

Contributors dc:contributor

• Franz Kurfess
• Computer Science
• College of Engineering

• Bot Detection
• Botnet Detection
• Web Scraping
• Data Science
• Statistics
• Unsupervised Learning
• Computer Sciences
• Information Security
• Statistics and Probability

Identifier

10.15368/theses.2021.10

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3792