Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detection of phishing webpages based on visual similarity
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
The battle against phishing: Dynamic Security Skins
SOUPS '05 Proceedings of the 2005 symposium on Usable privacy and security
Protecting Users Against Phishing Attacks with AntiPhish
COMPSAC '05 Proceedings of the 29th Annual International Computer Software and Applications Conference - Volume 01
The Wisdom of Crowds
Usage patterns of collaborative tagging systems
Journal of Information Science
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Web wallet: preventing phishing attacks by revealing user intentions
SOUPS '06 Proceedings of the second symposium on Usable privacy and security
Anomaly Based Web Phishing Page Detection
ACSAC '06 Proceedings of the 22nd Annual Computer Security Applications Conference
SuggestBot: using intelligent task routing to help people find work in wikipedia
Proceedings of the 12th international conference on Intelligent user interfaces
Cantina: a content-based approach to detecting phishing web sites
Proceedings of the 16th international conference on World Wide Web
Stronger password authentication using browser extensions
SSYM'05 Proceedings of the 14th conference on USENIX Security Symposium - Volume 14
Anti-Phishing Phil: the design and evaluation of a game that teaches people not to fall for phish
Proceedings of the 3rd symposium on Usable privacy and security
Examining the impact of website take-down on phishing
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
Fishing for phishes: applying capture-recapture methods to estimate phishing populations
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
A framework for detection and measurement of phishing attacks
Proceedings of the 2007 ACM workshop on Recurring malcode
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
You've been warned: an empirical study of the effectiveness of web browser phishing warnings
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On the Effectiveness of Techniques to Detect Phishing Sites
DIMVA '07 Proceedings of the 4th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Evaluating the Wisdom of Crowds in Assessing Phishing Websites
Financial Cryptography and Data Security
Visual-similarity-based phishing detection
Proceedings of the 4th international conference on Security and privacy in communication netowrks
A hybrid phish detection approach by identity discovery and keywords retrieval
Proceedings of the 18th international conference on World wide web
School of phish: a real-world evaluation of anti-phishing training
Proceedings of the 5th Symposium on Usable Privacy and Security
Financial incentives and the "performance of crowds"
Proceedings of the ACM SIGKDD Workshop on Human Computation
Security automation considered harmful?
NSPW '07 Proceedings of the 2007 Workshop on New Security Paradigms
Crowdsourcing graphical perception: using mechanical turk to assess visualization design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detecting visually similar Web pages: Application to phishing detection
ACM Transactions on Internet Technology (TOIT)
BogusBiter: A transparent protection against phishing attacks
ACM Transactions on Internet Technology (TOIT)
A hierarchical adaptive probabilistic approach for zero hour phish detection
ESORICS'10 Proceedings of the 15th European conference on Research in computer security
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Proceedings of the 2012 workshop on New security paradigms
| Hi-index | 0.00 |
Phishing is an ongoing kind of semantic attack that tricks victims into inadvertently sharing sensitive information. In this paper, we explore novel techniques for combating the phishing problem using computational techniques to improve human effort. Using tasks posted to the Amazon Mechanical Turk human effort market, we measure the accuracy of minimally trained humans in identifying potential phish, and consider methods for best taking advantage of individual contributions. Furthermore, we present our experiments using clustering techniques and vote weighting to improve the results of human effort in fighting phishing. We found that these techniques could increase coverage over and were significantly faster than existing blacklists used today.