Exploration of gaps in Bitly's spam detection and relevant counter measures
Neha Gupta, Ponnurangam Kumaraguru

TL;DR
This paper analyzes Bitly's spam detection effectiveness, identifies gaps in its current policies, and proposes a feature-based classification method that achieves over 86% accuracy in detecting malicious URLs.
Contribution
It is the first large-scale study to highlight issues in Bitly's spam detection and proposes a new classification approach with domain-specific features.
Findings
Identified key shortcomings in Bitly's spam detection policies.
Developed a feature-based classifier achieving 86.41% accuracy.
Highlighted the need for improved countermeasures against malicious URL propagation.
Abstract
Existence of spam URLs over emails and Online Social Media (OSM) has become a growing phenomenon. To counter the dissemination issues associated with long complex URLs in emails and character limit imposed on various OSM (like Twitter), the concept of URL shortening gained a lot of traction. URL shorteners take as input a long URL and give a short URL with the same landing page in return. With its immense popularity over time, it has become a prime target for the attackers giving them an advantage to conceal malicious content. Bitly, a leading service in this domain is being exploited heavily to carry out phishing attacks, work from home scams, pornographic content propagation, etc. This imposes additional performance pressure on Bitly and other URL shorteners to be able to detect and take a timely action against the illegitimate content. In this study, we analyzed a dataset marked as…
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Taxonomy
TopicsSpam and Phishing Detection · Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting
