article

Exploring user behavioral data for adaptive cybersecurity

Authors:

Joyce H. Addae,

Milena RadenkovicAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 29, Issue 3

Pages 701 - 750

https://doi.org/10.1007/s11257-019-09236-5

Published: 01 July 2019 Publication History

Abstract

This paper describes an exploratory investigation into the feasibility of predictive analytics of user behavioral data as a possible aid in developing effective user models for adaptive cybersecurity. Partial least squares structural equation modeling is applied to the domain of cybersecurity by collecting data on users' attitude towards digital security, and analyzing how that influences their adoption and usage of technological security controls. Bayesian-network modeling is then applied to integrate the behavioral variables with simulated sensory data and/or logs from a web browsing session and other empirical data gathered to support personalized adaptive cybersecurity decision-making. Results from the empirical study show that predictive analytics is feasible in the context of behavioral cybersecurity, and can aid in the generation of useful heuristics for the design and development of adaptive cybersecurity mechanisms. Predictive analytics can also aid in encoding digital security behavioral knowledge that can support the adaptation and/or automation of operations in the domain of cybersecurity. The experimental results demonstrate the effectiveness of the techniques applied to extract input data for the Bayesian-based models for personalized adaptive cybersecurity assistance.

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Exploring user behavioral data for adaptive cybersecurity
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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 29, Issue 3

July 2019

178 pages

ISSN:0924-1868

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Copyright © Copyright © 2019 Springer Nature B.V.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2019

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Cited By

Al-Emran MAl-Sharafi MForoughi BIranmanesh MAlsharida RAl-Qaysi NAli N(2024)Evaluating the barriers affecting cybersecurity behavior in the Metaverse using PLS-SEM and fuzzy sets (fsQCA)Computers in Human Behavior10.1016/j.chb.2024.108315159:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.chb.2024.108315
Yang YZhang SSun XZhang XSun XJing YYang C(2024)Exploring sex differences in collaborative virtual environments for participation equality and user experienceVirtual Reality10.1007/s10055-024-01022-y28:3Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1007/s10055-024-01022-y
Luo JZhang XFu FGeng H(2024)Alert Interaction Service Design for AI Face Swap Video ScamsHCI for Cybersecurity, Privacy and Trust10.1007/978-3-031-61379-1_17(265-280)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-61379-1_17
dos Santos Uda Costa CMayer Ados Reis EMaldonado JBarbosa JAntunes RRighi RFlores N(2022)Trends in User Identity and Continuous AuthenticationComputer10.1109/MC.2022.318727455:11(52-61)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/MC.2022.3187274
Pollini ACallari TTedeschi ARuscio DSave LChiarugi FGuerri D(2022)Leveraging human factors in cybersecurity: an integrated methodological approachCognition, Technology and Work10.1007/s10111-021-00683-y24:2(371-390)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10111-021-00683-y
Knisely BVaughn-Cooke MWagner LFink J(2021)Device personalization for heterogeneous populations: leveraging physician expertise and national population data to identify medical device patient user groupsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09305-831:5(979-1025)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11257-021-09305-8
Romero-Masters P(2021)The Model of Influence in Cybersecurity with FramesDiversity, Divergence, Dialogue10.1007/978-3-030-71305-8_18(225-234)Online publication date: 17-Mar-2021
https://dl.acm.org/doi/10.1007/978-3-030-71305-8_18

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