Psychological Distance in Cyber Decision Making:

Information about the Attackers

52nd Edwards Bayesian Research ConferenceFullerton, 15 February 2014

Jinshu Cui, Department of Psychology

Heather Rosoff, Sol Price School of Public Policy

Richard John, Department of Psychology

CREATE, University of Southern California

• Human operators are often thought of as a major cause of security failures - “the weakest link in the chain” [Schneier 2008]

• It is difficult for human operators to take cyber threats seriously when few cause serious consequences at the individual level

• Critical to understand perception and behavioral response to cyber threats

Evaluation of Cyber Threats

Identity theft?

Financial fraud?

Computer crash?

• Experience of a near miss significantly increased respondents’ endorsement of safer options, the effect was bigger under a gain frame than a loss frame.

• Experience of a hit significantly increased respondents’ endorsement of safer options relative to the near miss past experience.

• Experience of a false alarm significantly decreased respondents’ likelihood of endorsing safer response options, compared to the near miss past experience.

Rosoff, H., Cui, J., & John, R. S. (2013). Heuristics and biases in cyber security dilemmas. Environment Systems and Decisions, 33(4), 517-529.

Previous Research

Real Crime vs. Cyber Crime

• Personally targeted• Instant consequences• Have information about the

offender, have interaction with the offender, concern about the offender

• Group targeted• Delayed consequences• Rarely have information about

the attacker, have no interaction with the attacker, ignore the attacker

Who? Why?

• Construal level theory (CLT) – “distant” attacks will be viewed abstractly, and “proximal” attacks will be viewed concretely. (Trope & Liberman, 2003, 2010; Trope, Liberman, & Wakslak, 2007)

Motivation

Information about Attackers Attributes Psychological Distance Construal Level

attacker

identification

unknown most distant highest

group distant high

individual proximal low

physical identified individual most proximal lowest

attacker

motivations

unknown most distant highest

terrorism distant high

fame proximal low

money most proximal lowest

• Attacker identification

o group or individual

o physical identified or not

• Attack tactics

o personal accounto database

Experiment 1 – Research Questions

• Attacker Motivations

o money: purchase luxury itemso fame: increase his visibility and reputation within the

hacker communityo terrorism: provide financial support to a Middle Eastern

terrorist group

• Resolution Status

o resolvedo unresolved

Experiment 2 – Research Questions

• Financial attack scenario

• 4 (attacker identification) x 2 (attack tactics) between-subjects design

• Manipulations– Attacker identification:

• unknown• group• individual • individual with picture

– Attack tactics: database vs. personal account

Experiment 1 - Design

Official Bank Notification ___________________________________________________

August 2, 2013

Dear Valued Customer,       We are writing to notify you that two days ago, there was an unauthorized attempt to withdraw all of your current funds. (personal account) As of now, we know an individual online hacker is responsible for the breach into your account. (individual attacker) The hacker acted alone in carrying out the attack.

      We are working with law enforcement officials and regret any concern or inconvenience this incident may have caused you. We will keep you informed as we make progress in his capture. Kindest Regards,Your Bank

Experiment 1 – Measures

• 10-item PANAS– 1 (not at all) to 5 (extremely)– 5-item negative affect: α = 0.94– 5-item positive affect: α = 0.84

• 4-item Risk Perception: – 0 to 10 / 0% to 100%– α = 0.83

• 8-item Behavioral Intention: – 1 (strongly disagree) to 5 (strongly agree)– 3-item stay with bank: α = 0.63– 3-item stay away from bank: α = 0.75

• Recruited from Amazon Mechanical Turk• N = 239• $0.55 each• Median time to complete: 6 min• 43 % female• 50% 18-30 years old• 98.3% shop online, 92.9% bank online

Experiment 1 – Respondents

Less negative affect associated with pictured individual attacker compared to individual attacker without a picture (p = .038)

low psychological distance would increase participants’ interest in subordinate and secondary aspects (Liviatan, Trope, and Liberman, 2008)

Experiment 1 – Negative Affect

individual individual with picture2

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8 Mean Score of Negative Affect

database

personal

attacker identification

Mea

n N

egati

ve A

ffect

(1-5

)

attack tactics

More positive affect was experienced if a personal account was attacked compared to a database (p = .024)

Experiment 1 – Positive Affect

database personal1

1.2

1.4

1.6

1.8

2

2.2

2.4 Mean Score of Positive Affect

group

individual

individual with picture

unknown

attacker identification

Mea

n Po

sitive

Affe

ct (1

-5)

attack tac-tics

When database was attacked, respondents are more willing to count on the bank when the attacker was physically identified; with an individual account attacked, there is little difference. (p = 0.036)

Experiment 1 – Protective Behavior

database personal4

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8 Mean Score of Expectation on Bank

individual

individual with picture

attacker identification

Expe

ctati

on o

n Ba

nk (1

-5)

attack tactics

Female respondents tended to experience more negative affect (p = .014), higher perceived risk (p = .022), and were more likely to support for government’s intervention for online protection (p = .021) (Hale, 1996)

Experiment 1 – Sex as a Moderator

• Identity theft scenario• 4 (perpetrator’s motivation) x 3 (resolution status)

between-subjects design• Manipulations

– Perpetrator’s motivation: • fame• money • terrorism• unknown

– Resolution status: • resolved • unresolved• unknown

Experiment 2 - Design

Scene 1: This morning in the mail you received a credit card statement in your name from a company with which you do not have an account.  As you looked over the statement, you noticed several cash advances totaling $500. (PANAS)

Scene 2: One week following your receipt of the suspicious credit card statement, you receive the following voice mail:

“Good morning, my name is Gabriel Dawson from the Identity Theft Unit of the Police Department. Our investigation into a cyber perpetrator has led us to believe your personal computer has been compromised. We believe this individual hacked into your computer and obtained access to your email account and the cache data of your online activities. In doing so, he was able to obtain your usernames, passwords, banking information, and other personal information.  Our investigation thus far shows no evidence that can confirm the perpetrator's intent. (unknown motivation) I plan to be in touch in the coming weeks to report on the progress of our investigation. Please be vigilant in reporting to us any suspicious mail, email, or phone call. Thank you.“ (PANAS, risk perception, short-term behavior)

Experiment 2 – Scenes 1 and 2

Scene 3: In the days following the call from the Identity Theft Unit, you notice an increase in suspicious activity. You are receiving more spam emails, junk mails and phone calls from solicitors. More notably is your receipt of a phone call from the Department of Motor Vehicles confirming the issuance of a new driver's license you did not order. You also receive a letter in the mail from the Internal Revenue Service inquiring about your filing of duplicate income tax returns, suggesting that fraudulent returns were submitted in your name. (PANAS)

Scene 4: Moving ahead to several weeks following the call from the Identity Theft Unit of the Police Department, you receive yet another credit card statement in the mail from a company with which you do not have an account. This statement has a $1,500 balance. (unresolved) It is clear that you are continuing to experience complications as a result of your identity theft and that you are still at risk. (PANAS, risk perception, long-term behavior)

Experiment 2 – Scenes 3 and 4

• 10-item negative affect (from PANAS): – 1 (not at all) to 5 (extremely) – 8-item negative affect (4 time periods): α = 0.93, 0.92, 0.92, 0.94

• 8-item Risk Perception: – 1 (strongly disagree) to 6 (strongly agree)– 5-item risk perception (2 time periods): α = 0.81, 0.83

• 10-item short-term behavior: – check all that apply– Summed number of checked responses

• 12-item long-term behavior: – 1 (strongly disagree) to 6 (strongly agree)– 9-item long-term behavior: α = 0.86

Experiment 2 - Measures

• Recruited from Amazon Mechanical Turk• N = 419• $0.75 each• Median time to complete: 7 min• 44 % Female• 50% 18-29 years old• 72% have at least one credit card, of which:

– 8% have had an account opened fraudulently in their name

– 6% pay for an identity theft protection service

Experiment 2 - Respondents

Respondents experienced less negative affect when the identity theft case was resolved compared to unresolved or unknown

Experiment 2 – Negative Affect

resolved unknown unresolved1.5

2

2.5

3

3.5

4Mean Score of Negative Affect

(Scene 4)

fame

money

terrorism

unknown

resolution status

Neg

ative

Affe

ct (1

-5)

perpetrator's motivation

Respondents perceived less risk of identity theft when the perpetrator was to fund terrorism compared to for money or fame

Participants in the low psychological distance condition reported higher risk perceptions (Chandran&Menon, 2004)

Experiment 2 – Risk Perception

fame money terrorism4.5

4.6

4.7

4.8

4.9

5

5.1

Mean Score of Risk Perception (scene 2)

resolution status

Risk

Per

cepti

on (1

-6)

Respondents perceived less risk of identity theft when the situation was resolved compared to unresolved or unknown

Experiment 2 – Risk Perception

resolved unknown unresolved4

4.2

4.4

4.6

4.8

5

5.2

5.4

Mean Score of Risk Perception (scene 4)

money

terrorism

resolution status

Risk

Per

cepti

on (1

-6)

perpetrator's motivation

Participants are more willing to pursue long-term behavior of online identity protection when the identity theft case was unresolved or unknown than if it was resolved.

Experiment 2 – Long Term Protective Behavior

resolved unknown unresolved2

2.5

3

3.5

4

4.5

Mean Score of Long-term Behavior(Scene 4)

money

terrorism

resolution status

Mea

n Lo

ng-te

rm B

ehav

ior (

1-6)

perpetrator's motivation

Female participants tended to experience more negative affect, high perceived risk, were more likely to seek help (short-term behavior) and more likely to pursue online identity protection (long-term behavior)

Experiment 2 – Sex as a Moderating Variable

Conclusions

• Cyber attacker and attack characteristics influence respondents’ affective responses, risk perceptions, and intended long term behavior

• Cyber Attacker Identification (Individual, Group, Individual with Picture, UK)

• Cyber Attack Tactics (Personal account vs. Database)• Cyber Attackers’ Motivations (Fame, Money, Terror, UK)• Resolution of Cyber Attack (Resolved, Unresolved, UK)

Psychological Distance in Cyber Decision Making:

Information about the Attackers

52nd Edwards Bayesian Research ConferenceFullerton, 15 February 2014

Jinshu Cui, Department of Psychology

Richard John, Department of Psychology

Heather Rosoff, Sol Price School of Public Policy

CREATE, University of Southern California

Overview

• Research Questions– Do attacker identification (e.g., picture or not), attack

tactics (i.e., personal account or database), motivations of the perpetrator (e.g., money, terrorism), or resolution of the event influence emotional, cognitive and behavioral responses?

• Experiment 1 – Financial Fraud: attacker identification, attack tactics

• Experiment 2 – Identity Theft: perpetrator’s motivation, resolution status