Dinei Florencio and Cormac Herley
We propose a scheme that exploits scale to prevent phishing. We show that while stopping phishers from obtaining passwords is very hard, detecting the fact that a password has been entered at an unfamiliar site is simple. Our solution involves a client that reports Password Re-Use (PRU) events at unfamiliar sites, and a server that accumulates these reports and detects an attack. We show that it is simple to then mitigate the damage by communicating the identities of phished accounts to the institution under attack. Thus, we make no attempt to prevent information leakage, but we try to detect and then rescue users from the consequences of bad trust decisions. The scheme requires deployment on a large scale to realize the major benefits: reliable low latency detection of attacks, and mitigation of compromised accounts. We harness scale against the attacker instead of trying to solve the problem at each client. In  we sketched the idea, but questions relating to false positives and the scale required for efficacy remained unanswered. We present results from a trial deployment of half a million clients. We explain the scheme in detail, analyze its performance, and examine a number of anticipated attacks.
|Published in||Proc. Anti-phishing Working Group 2nd Annual eCrime Researchers Summit|
|Publisher||Association for Computing Machinery, Inc.|
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or firstname.lastname@example.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.