TabsGuard: A Hybrid Approach to Detect and Prevent Tabnabbing Attacks

Abstract

Phishing is one of the most prevalent types of modern attacks, costing significant financial losses to enterprises and users each day. Despite the emergence of various anti-phishing tools, not only there has been a dramatic increase in the number of phishing attacks but also more sophisticated forms of these attacks have come into existence. One of these forms of phishing attacks is the tabnabbing attack. Tabnabbing takes advantage of the user’s trust and inattention to the open tabs in the browser and changes the appearance of an already open malicious page to the appearance of a trusted website. The existing tabnabbing detection and prevention techniques block scripts that are susceptible to perform malicious actions or violate the browser security policy. However, most of these techniques cannot effectively prevent the script-free variant of the tabnabbing attack. In this paper, we introduce TabsGuard, an approach that combines heuristics and a machine-learning technique to keep track of the major changes made to the layout of a webpage whenever a tab loses its focus. TabsGuard is developed as a browser extension and evaluated against the top 1,000 trusted websites from Alexa. The results of our evaluation convey a significant improvement over the existing techniques. Finally, TabsGuard can be deployed as an extension service as a viable means for protecting against tabnabbing attacks.