Smartening the crowds

Abstract

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.