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Journal Article

Citation

Embrey I, Kaivanto K. Risk Anal. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Society for Risk Analysis, Publisher John Wiley and Sons)

DOI

10.1111/risa.13947

PMID

35568794

Abstract

Normative decision theory proves inadequate for modeling human responses to the social-engineering campaigns of advanced persistent threat (APT) attacks. Behavioral decision theory fares better, but still falls short of capturing social-engineering attack vectors which operate through emotions and peripheral-route persuasion. We introduce a generalized decision theory, under which any decision will be made according to one of multiple coexisting choice criteria. We denote the set of possible choice criteria by C. Thus, the proposed model reduces to conventional Expected Utility theory when |CEU| = 1 , while Dual-Process (thinking fast vs. thinking slow) decision making corresponds to a model with |CDP| = 2. We consider a more general case with |C| ≥ 2 , which necessitates careful consideration of how, for a particular choice-task instance, one criterion comes to prevail over others. We operationalize this with a probability distribution that is conditional upon traits of the decisionmaker as well as upon the context and the framing of choice options. Whereas existing signal detection theory (SDT) models of phishing detection commingle the different peripheral-route persuasion pathways, in the present descriptive generalization the different pathways are explicitly identified and represented. A number of implications follow immediately from this formulation, ranging from the conditional nature of security-breach risk to delineation of the prerequisites for valid tests of security training. Moreover, the model explains the "stepping-stone" penetration pattern of APT attacks, which has confounded modeling approaches based on normative rationality.


Language: en

Keywords

advanced persistent threat; choice criteria; dual-process theory; latent class model; peripheral-route persuasion; phishing; social engineering; states of mind

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