@conference {1970506,
	title = {Identifying potentially risky insider on-compliance using machine learning to assess multiple protection motivation behaviors},
	booktitle = {WISP2021: 2021 Workshop on Information Security and Privacy (WISP)},
	year = {2019},
	month = {2019},
	abstract = {Cybersecurity researchers have made significant steps to understand the mechanisms of security policy compliance and unify theories of security behavior. However, due partly to the limitations of traditional variance model statistical methods, these studies by necessity typically focus on a single security policy issue. By contrast, new machine learning algorithms frequently employed by data scientists offer great promise as a new statistical approach for examining robust individualized interpretations of policy and can also identify potentially risky behaviors. This study proposes to explore cybersecurity training impediments of multiple protection motivation behaviors in ransomware prevention training. It demonstrates the feasibility of using machine learning with survey items from the cybersecurity research to predict non-compliance. It also illustrates a potentially novel method to statistically validate research theory through higher levels of ML prediction. This study is a work in progress and we seek feedback on its design and relevance.},
	keywords = {Accounting, BIS},
	url = {https://aisel.aisnet.org/wisp2019/1},
	author = {Curry,Michael and Marshall,Byron and Crossler,Robert E}
}
@article {1970511,
	title = {InfoSec Process Action Model (IPAM): Targeting Insider{\textquoteright}s Weak Password Behavior},
	journal = {Journal of Information Systems},
	volume = {33},
	year = {2019},
	month = {2019},
	pages = {201-225},
	abstract = {The possibility of noncompliant behavior is a challenge for cybersecurity professionals and their auditors as they try to estimate residual control risk. Building on the recently proposed InfoSec Process Action Model (IPAM), this work explores how nontechnical assessments and interventions can indicate and reduce the likelihood of risky individual behavior. The multi-stage approach seeks to bridge the well-known gap between intent and action. In a strong password creation experiment involving 229 participants, IPAM constructs resulted in a marked increase in R<sup>2</sup> for initiating compliance behavior with control expectations from 47 percent to 60 percent. Importantly, the model constructs offer measurable indications despite practical limitations on organizations{\textquoteright} ability to assess problematic individual password behavior. A threefold increase in one measure of strong password behavior suggested the process positively impacted individual cybersecurity behavior. The results suggest that the process-nuanced IPAM approach is promising both for assessing and impacting security compliance behavior.},
	keywords = {Accounting, BIS},
	url = {https://doi.org/10.2308/isys-52381},
	author = {Curry,Michael and Marshall,Byron and Correia,John and Crossler,Robert E}
}
@article {1970516,
	title = {InfoSec Process Action Model (IPAM): Systematically Addressing Individual Security Behavior},
	journal = {Data Base for Advances in Information Systems},
	volume = {49},
	year = {2018},
	month = {2018},
	abstract = {While much of the extant InfoSec research relies on single assessment models that predict intent to act, this article proposes a multi-stage InfoSec Process Action Model (IPAM) that can positively change individual InfoSec behavior. We believe that this model will allow InfoSec researchers to focus more directly on the process which leads to action and develop better interventions that address problematic security behaviors. Building on successful healthcare efforts which resulted in smoking cessation, regular exercise and a healthier diet, among others, IPAM is a hybrid, predictive, process approach to behavioral InfoSec improvement. IPAM formulates the motivational antecedents of intent as separate from the volitional drivers of behavior. Singular fear appeals often seen in InfoSec research are replaced by more nuanced treatments appropriately differentiated to support behavioral change as part of a process; phase-appropriate measures of self-efficacy are employed to more usefully assess the likelihood that a participant will act on good intentions; and decisional balance {\textendash}assessment of pro and con perceptions {\textendash} is monitored over time. These notions better align InfoSec research to both leading security practice and to successful comparators in healthcare. We believe IPAM can both help InfoSec research models better explain actual behavior and better inform practical security-behavior improvement initiatives.},
	keywords = {Accounting, BIS},
	url = {https://www.researchgate.net/publication/321138048_InfoSec_Process_Action_Model_IPAM_Systematically_Addressing_Individual_Security_Behavior},
	author = {Curry,Michael and Marshall,Byron and Crossler,Robert E and Correia,John}
}
