Explanatory and predictive modeling of cybersecurity behaviors using protection motivation theory

IF 4.8 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computers & Security Pub Date : 2024-11-09 DOI:10.1016/j.cose.2024.104204

Uzma Kiran , Naurin Farooq Khan , Hajra Murtaza , Ali Farooq , Henri Pirkkalainen

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引用次数: 0

Abstract

Context

Protection motivation theory (PMT) is the most frequently used theory in understanding cyber security behaviors. However, most studies have used a cross-sectional design with symmetrical analysis techniques such as structure equation modeling (SEM) and regression. A data-driven approach, such as predictive modeling, is lacking and can potentially evaluate and validate the predictive power of PMT for cybersecurity behaviors.

Objective

The objective of this study is to assess the explanatory and predictive power of PMT for cyber security behaviors related to computers and smartphone.

Method

An online survey was employed to collect data from 1027 participants. The relationship of security behaviors with threat appraisal (severity and vulnerability) and coping appraisal (response efficacy, self-efficacy and response cost) components were tested via explanatory and predictive modeling. Explanatory modeling was employed via SEM, whereas three machine learning algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) were used for predictive modeling. Wrapper feature selection was employed to understand the most important factors of PMT in predictive modeling.

Results

The results revealed that the threat severity from the threat appraisal component of PMT significantly influenced computer security and smartphone security behaviors. From the coping appraisal, response efficacy and self-efficacy significantly influenced computer and smartphone security behaviors. The ML analysis showed that the highest predictive power of PMT for computer security was 76 % and for smartphone security 68 % by KNN algorithm. The wrapper feature selection approach revealed that the most important features in predicting security behaviors are self-efficacy, response efficacy and intention to secure devices. Thus, the findings indicate the complementarity of the cross-sectional and data driven methods.

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利用保护动机理论对网络安全行为进行解释和预测建模

背景保护动机理论（PMT）是理解网络安全行为最常用的理论。然而，大多数研究都采用了横断面设计和对称分析技术，如结构方程建模（SEM）和回归分析。本研究旨在评估 PMT 对与计算机和智能手机相关的网络安全行为的解释力和预测力。通过解释性和预测性建模，检验了安全行为与威胁评价（严重性和脆弱性）和应对评价（应对效能、自我效能和应对成本）的关系。解释性建模通过 SEM 进行，而预测性建模则使用了三种机器学习算法，即决策树（DT）、支持向量机（SVM）和 K 最近邻（KNN）。结果表明，PMT 中威胁评估部分的威胁严重程度显著影响了计算机安全和智能手机安全行为。在应对评估中，反应效能和自我效能对计算机和智能手机安全行为有显著影响。ML 分析表明，通过 KNN 算法，PMT 对计算机安全的最高预测能力为 76%，对智能手机安全的最高预测能力为 68%。包装特征选择方法显示，预测安全行为的最重要特征是自我效能感、响应效能感和确保设备安全的意愿。因此，研究结果表明了横截面方法和数据驱动方法的互补性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers & Security 工程技术-计算机：信息系统

CiteScore

12.40

自引率

7.10%

发文量

365

审稿时长

10.7 months

期刊介绍： Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.