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Psychological Distance in Cyber Decision Making: Information about the Attackers. 52nd Edwards Bayesian Research Conference Fullerton, 15 February 2014 Jinshu Cui, Department of Psychology Heather Rosoff , Sol Price School of Public Policy Richard John, Department of Psychology

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psychological distance in cyber decision making information about the attackers

Psychological Distance in Cyber Decision Making: Information about the Attackers

52nd Edwards Bayesian Research Conference

Fullerton, 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

evaluation of cyber threats
Evaluation of Cyber Threats

Identity theft?

Financial fraud?

Computer crash?

  • 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
previous research
Previous Research
  • 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.

real crime vs cyber crime
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?
motivation

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
experiment 1 research questions

Attacker identification

group or individual

physical identified or not

Attack tactics

personal account

database

Experiment 1 – Research Questions
experiment 2 research questions

Attacker Motivations

money: purchase luxury items

fame: increase his visibility and reputation within the hacker community

terrorism: provide financial support to a Middle Eastern terrorist group

Resolution Status

resolved

unresolved

Experiment 2 – Research Questions
experiment 1 design
Experiment 1 - Design
  • 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
slide9

Official Bank Notification

___________________________________________________

August 2, 2013Dear 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
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
experiment 1 respondents
Experiment 1 – Respondents
  • 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 negative affect
Experiment 1 – Negative Affect

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 positive affect
Experiment 1 – Positive Affect

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

experiment 1 protective behavior
Experiment 1 – Protective Behavior

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 sex as a moderator
Experiment 1 – Sex as a Moderator

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 2 design
Experiment 2 - Design
  • 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 scenes 1 and 2
Experiment 2 – Scenes 1 and 2

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 3 and 4
Experiment 2 – Scenes 3 and 4

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 measures
Experiment 2 - Measures
  • 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 respondents
Experiment 2 - Respondents
  • 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 negative affect
Experiment 2 – Negative Affect

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

experiment 2 risk perception
Experiment 2 – Risk Perception

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 perception1
Experiment 2 – Risk Perception

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

experiment 2 long term protective behavior
Experiment 2 – Long Term Protective Behavior

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 sex as a moderating variable
Experiment 2 – Sex as a Moderating Variable

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)

conclusions
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 attackers1

Psychological Distance in Cyber Decision Making: Information about the Attackers

52nd Edwards Bayesian Research Conference

Fullerton, 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
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