Article

Why phishing works

Authors:

Rachna Dhamija,

Marti HearstAuthors Info & Claims

CHI '06: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 581 - 590

https://doi.org/10.1145/1124772.1124861

Published: 22 April 2006 Publication History

Abstract

To build systems shielding users from fraudulent (or phishing) websites, designers need to know which attack strategies work and why. This paper provides the first empirical evidence about which malicious strategies are successful at deceiving general users. We first analyzed a large set of captured phishing attacks and developed a set of hypotheses about why these strategies might work. We then assessed these hypotheses with a usability study in which 22 participants were shown 20 web sites and asked to determine which ones were fraudulent. We found that 23% of the participants did not look at browser-based cues such as the address bar, status bar and the security indicators, leading to incorrect choices 40% of the time. We also found that some visual deception attacks can fool even the most sophisticated users. These results illustrate that standard security indicators are not effective for a substantial fraction of users, and suggest that alternative approaches are needed.

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Cited By

Agnes Kikelomo AIsrael Oludayo O(2024)Development of a Phishing Detection System Using Support Vector MachineInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24MAY353(247-257)Online publication date: 17-May-2024
https://doi.org/10.38124/ijisrt/IJISRT24MAY353
Kwizera IMicheal S(2024)Developing a Multi-Layered Defence System to Safeguard Data against Phishing AttacksInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24FEB1107(2022-2033)Online publication date: 5-Apr-2024
https://doi.org/10.38124/ijisrt/IJISRT24FEB1107
Aslam SAslam HManzoor AChen HRasool A(2024)AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL DetectionSymmetry10.3390/sym1602024816:2(248)Online publication date: 17-Feb-2024
https://doi.org/10.3390/sym16020248
Show More Cited By

Index Terms

Why phishing works
1. Applied computing
  1. Electronic commerce
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

CHI '06: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2006

1353 pages

ISBN:1595933727

DOI:10.1145/1124772

Editors:
Rebecca Grinter
Georgia Institute of Technology, USA
,
Thomas Rodden
University of Nottingham, UK
,
Paul Aoki
Intel, USA
,
Ed Cutrell
Microsoft, USA
,
Robin Jeffries
Google, USA
,
Gary Olson
University of Michigan, USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part 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, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 April 2006

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CHI06: CHI 2006 Conference on Human Factors in Computing Systems

April 22 - 27, 2006

Québec, Montréal, Canada

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Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Agnes Kikelomo AIsrael Oludayo O(2024)Development of a Phishing Detection System Using Support Vector MachineInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24MAY353(247-257)Online publication date: 17-May-2024
https://doi.org/10.38124/ijisrt/IJISRT24MAY353
Kwizera IMicheal S(2024)Developing a Multi-Layered Defence System to Safeguard Data against Phishing AttacksInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24FEB1107(2022-2033)Online publication date: 5-Apr-2024
https://doi.org/10.38124/ijisrt/IJISRT24FEB1107
Aslam SAslam HManzoor AChen HRasool A(2024)AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL DetectionSymmetry10.3390/sym1602024816:2(248)Online publication date: 17-Feb-2024
https://doi.org/10.3390/sym16020248
Trad FChehab A(2024)Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language ModelsMachine Learning and Knowledge Extraction10.3390/make60100186:1(367-384)Online publication date: 6-Feb-2024
https://doi.org/10.3390/make6010018
Cao YDhekne AAmmar MKim YKim JKoushanfar FRasmussen K(2024)UWB-Auth: A UWB-based Two Factor Authentication PlatformProceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3643833.3656113(185-195)Online publication date: 27-May-2024
https://dl.acm.org/doi/10.1145/3643833.3656113
Burda PAllodi LZannone N(2024)Cognition in Social Engineering Empirical Research: A Systematic Literature ReviewACM Transactions on Computer-Human Interaction10.1145/363514931:2(1-55)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3635149
Pourmohamad RWirsz SOest ABao TShoshitaishvili YWang RDoupé ABazzi RQuek TGao DZhou JCardenas A(2024)Deep Dive into Client-Side Anti-Phishing: A Longitudinal Study Bridging Academia and IndustryProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3657027(638-653)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3657027
Jiang HJi PZhang TCao HLiu D(2024)Two-Factor Authentication for Keyless Entry System via Finger-Induced VibrationsIEEE Transactions on Mobile Computing10.1109/TMC.2024.336833123:10(9708-9720)Online publication date: Oct-2024
https://doi.org/10.1109/TMC.2024.3368331
Huang LZhu Q(2024)ZETAR: Modeling and Computational Design of Strategic and Adaptive Compliance PoliciesIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332353911:3(4001-4015)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3323539
Rahman AAl-Obeidat FTubaishat AShah BAnwar SHalim Z(2024)Discovering the Correlation Between Phishing Susceptibility Causing Data Biases and Big Five Personality Traits Using C-GANIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.320115311:4(4800-4808)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2022.3201153
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