Learning of personalized security settings

Sharifi, Mehrbod; Fink, Eugene; Carbonell, Jaime G.

doi:10.1109/icsmc.2010.5642461

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…As another example, to increase warning compliance, browsers could use different indicators to inform users in different locations that they are visiting malicious websites. In a similar vein, information about users' location could help set default system security settings [43].…”

Section: Personalized Security Toolsmentioning

confidence: 99%

Self-Confidence Trumps Knowledge

Sawaya

Sharif

Christin

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Computer security tools usually provide universal solutions without taking user characteristics (origin, income level, ...) into account. In this paper, we test the validity of using such universal security defenses, with a particular focus on culture. We apply the previously proposed Security Behavior Intentions Scale (SeBIS) to 3,500 participants from seven countries. We first translate the scale into seven languages while preserving its reliability and structure validity. We then build a regression model to study which factors affect participants' security behavior. We find that participants from different countries exhibit different behavior. For instance, participants from Asian countries, and especially Japan, tend to exhibit less secure behavior. Surprisingly to us, we also find that actual knowledge influences user behavior much less than user self-confidence in their computer security knowledge. Stated differently, what people think they know affects their security behavior more than what they do know.

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Section: Personalized Security Toolsmentioning

confidence: 99%

Self-Confidence Trumps Knowledge

Sawaya

Sharif

Christin

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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SmartNotes: Application of crowdsourcing to the detection of web threats

Sharifi

Fink

Carbonell

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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We describe a crowdsourcing system, called SmartNotes, which detects security threats related to web browsing, such as Internet scams, deceptive sales of substandard products, and websites with intentionally misleading information. It combines automatically collected data about websites with user votes and comments, and uses them to identify potential threats. We have implemented it as a browser extension, which is available for free public use.

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Detection of Internet scam using logistic regression

Sharifi

Fink

Carbonell

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Internet scam is fraudulent or intentionally misleading information posted on the web, usually with the intent of tricking people into sending money or disclosing sensitive information. We describe an application of logistic regression to detection of Internet scam. The developed system automatically collects 43 characteristic statistics of the websites from 11 online sources and computes the probability that a given website is malicious. We present an empirical evaluation of this system, which shows that its precision and recall are 98%.

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Learning of personalized security settings

Cited by 3 publications

References 11 publications

Self-Confidence Trumps Knowledge

Self-Confidence Trumps Knowledge

SmartNotes: Application of crowdsourcing to the detection of web threats

Detection of Internet scam using logistic regression

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