Client-Side Defense Against Web-Based Identity Theft

By N. Chou, R. Ledesma, Y. Teraguchi, John C. Mitchell. 2004

539 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Brad Wardman
2. Steve Sheng
3. Xiao Han
4. Justin Ma
5. Gary Warner
6. Srishti Gupta
7. Davide Balzarotti
8. Nizar Kheir

Citations in the corpus

• PhishEye: Live Monitoring of Sandboxed Phishing Kits (2016, background): “Several studies propose a browser plugin to protect users from phishing [6, 9, 20].”
• Phishing Detection: A Literature Survey (2013, background): “SpoofGuard [30], a web browser plug-in developed by Stanford University, detects HTTP(S)-based phishing attempts as a web browser toolbar, by weighting certain anomalies found in the HTML content against a defined threshold value.”
• You're Not Who You Claim to Be: Website Identity Check for Phishing Detection (2010, methodology): “Various approaches have been used for phishing website detection, including blacklists [10], whitelists [2], heuristics [3], and classifications [21], [6], [7], [19], [9].”
• On the Effectiveness of Techniques to Detect Phishing Sites (2007, background): “Examples of properties are the number of password fields, the number of links, or the number of unencrypted password fields (these are properties used by SpoofGuard [3]).”
• An Anti-Phishing System Employing Diffused Information (2014, methodology): “We further analyze current anti-phishing warnings from the perspective of warning theory, and propose a new warning design employing our Gestalt approach.”
• Detecting visually similar Web pages: Application to phishing detection (2010, background): “IE7 [Microsoft 2009], FireFox3 [Mozilla 2008], Spoofguard [Chou et al. 2004], and Netcraft [2009] are popular toolbars in wide usage.”
• Identifying suspicious URLs: an application of large-scale online learning (2009, background): “We are motivated by prior studies noting distinguishing commonalities in such features for malicious URLs (Chou et al., 2004; McGrath & Gupta, 2008).”
• A framework for detection and measurement of phishing attacks (2007, methodology): “Their evaluation suggests that SpoofGuard was very e.ective in detection of phishing sites, however it did have a very high false positive rate.”
• Emerging phishing trends and effectiveness of the anti-phishing landing page (2014, background): “Several other toolbars like SpoofGaurd [10], TrustBar [20], PhishZoo [1], Netcraft [33], and SiteAdvisor [29] were developed to warn users about phishing attacks.”
• An Empirical Analysis of Phishing Blacklists (2009, background): “A number of these blacklists are used in integrated browser phishing protection [4, 15, 25], and in web browser toolbars [6, 7, 28].”
• Anomaly Based Web Phishing Page Detection (2006, methodology): “SpoofGuard [3] uses domain names, URLs, links, and images to measure the similarity between a given page and the pages in the caches or histories.”