語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Online Scam Detection Using Human Psychology: Toward Usable Cybersecurity /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Online Scam Detection Using Human Psychology: Toward Usable Cybersecurity // Sarah Ying Zheng.
作者:
Zheng, Sarah Ying,
面頁冊數:
1 electronic resource (167 pages)
附註:
Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
Contained By:
Dissertations Abstracts International85-07A.
標題:
Criminology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30989976
ISBN:
9798381418514
Online Scam Detection Using Human Psychology: Toward Usable Cybersecurity /
Zheng, Sarah Ying,
Online Scam Detection Using Human Psychology: Toward Usable Cybersecurity /
Sarah Ying Zheng. - 1 electronic resource (167 pages)
Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
Online scams are taking an emotional and financial toll on people around the globe. Current scam prevention methods thus are falling short. This thesis aims to understand why people are bad at detecting online scams and develop interventions to help people improve, using phishing e-mails as quintessential scam examples.It first presents a fundamental advance in understanding why people may have difficulties discerning honesty in online contexts. Using computational methods and a novel task, we find that worse discernment is driven by how people weight heuristics versus more cognitively demanding computations. Specifically, over-reliance on one's own behaviour led to worse discernment and higher reliance on statistical probabilities led to more accurate discernment. This finding is then applied to improve people's ability to detect phishing e-mails. People may not recognise phishing scams, because most people do not create phishing themselves. Our results suggest that engaging people with how to write phishing e-mails indeed improves detection. Next, we find that people's phishing detection ability is not related to demographic factors, user interaction styles, nor negligence of e-mail sender details. Instead, poor phishing detection related to people's lacking understanding of technical legitimacy cues and widely differing communication norms. We then show promising directions for user-centric e-mail security tools that enhance intuitive cues of legitimacy.These studies demonstrate that a deeper understanding of why people may fail to detect online scams can help develop new methods to reduce victimisation. Specifically, online legitimacy judgements may be improved by (i) policies that enforce media to recommend content that reflect true statistics of real-world phenomena, (ii) teaching people how online scams are created, and (iii) highlighting trust cues and norms for digital conduct. Individual risk-based approaches such as targeted interventions based on demographics are unwarranted by the current research.
English
ISBN: 9798381418514Subjects--Topical Terms:
563146
Criminology.
Subjects--Index Terms:
Deception
Online Scam Detection Using Human Psychology: Toward Usable Cybersecurity /
LDR
:03557nam a22004693i 4500
001
1157741
005
20250603111406.5
006
m o d
007
cr|nu||||||||
008
250804s2024 miu||||||m |||||||eng d
020
$a
9798381418514
035
$a
(MiAaPQD)AAI30989976
035
$a
AAI30989976
040
$a
MiAaPQD
$b
eng
$c
MiAaPQD
$e
rda
100
1
$a
Zheng, Sarah Ying,
$e
author.
$3
1484004
245
1 0
$a
Online Scam Detection Using Human Psychology: Toward Usable Cybersecurity /
$c
Sarah Ying Zheng.
264
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2024
300
$a
1 electronic resource (167 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
500
$a
Advisors: Becker, Ingolf; Sharot, Tali.
502
$b
Ph.D.
$c
University of London, University College London (United Kingdom)
$d
2024.
520
$a
Online scams are taking an emotional and financial toll on people around the globe. Current scam prevention methods thus are falling short. This thesis aims to understand why people are bad at detecting online scams and develop interventions to help people improve, using phishing e-mails as quintessential scam examples.It first presents a fundamental advance in understanding why people may have difficulties discerning honesty in online contexts. Using computational methods and a novel task, we find that worse discernment is driven by how people weight heuristics versus more cognitively demanding computations. Specifically, over-reliance on one's own behaviour led to worse discernment and higher reliance on statistical probabilities led to more accurate discernment. This finding is then applied to improve people's ability to detect phishing e-mails. People may not recognise phishing scams, because most people do not create phishing themselves. Our results suggest that engaging people with how to write phishing e-mails indeed improves detection. Next, we find that people's phishing detection ability is not related to demographic factors, user interaction styles, nor negligence of e-mail sender details. Instead, poor phishing detection related to people's lacking understanding of technical legitimacy cues and widely differing communication norms. We then show promising directions for user-centric e-mail security tools that enhance intuitive cues of legitimacy.These studies demonstrate that a deeper understanding of why people may fail to detect online scams can help develop new methods to reduce victimisation. Specifically, online legitimacy judgements may be improved by (i) policies that enforce media to recommend content that reflect true statistics of real-world phenomena, (ii) teaching people how online scams are created, and (iii) highlighting trust cues and norms for digital conduct. Individual risk-based approaches such as targeted interventions based on demographics are unwarranted by the current research.
546
$a
English
590
$a
School code: 6022
650
4
$a
Criminology.
$3
563146
650
4
$a
Web studies.
$3
1148502
650
4
$a
Psychology.
$3
555998
650
4
$a
Computer science.
$3
573171
653
$a
Deception
653
$a
Detection
653
$a
Fraud
653
$a
Lying
653
$a
Phishing
653
$a
Scams
690
$a
0984
690
$a
0621
690
$a
0646
690
$a
0627
710
2
$a
University of London, University College London (United Kingdom).
$b
Science and Technology Studies.
$3
1437717
720
1
$a
Becker, Ingolf
$e
degree supervisor.
720
1
$a
Sharot, Tali
$e
degree supervisor.
773
0
$t
Dissertations Abstracts International
$g
85-07A.
790
$a
6022
791
$a
Ph.D.
792
$a
2024
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30989976
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入