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A Data‐Driven Multi‐Factor Model for Android Mobile Security Risk Assessment

Year: 2026
Type: article
Study design: Observational Study
Abstract: ABSTRACT Android is one of the most widely adopted mobile operating systems and, because of its extensive application ecosystem and large user base, remains an important target for cybersecurity threats. This study investigates the factors associated with respondents' perceived likelihood of increases in Android‐related threats and in Android device security, using a structured questionnaire‐based survey. A total of 403 valid responses have been collected from Android users belonging to different professional groups. Principal component analysis (PCA) has been used to identify meaningful security‐related components from the questionnaire items, followed by multiple linear regression and binary logistic regression to examine the relationships between the identified factors and Android device security and respondents' perceived likelihood of an increase in Android‐related threats. The findings indicate that selected security‐related factors have statistically significant relationships with device security and respondents' perceived likelihood of an increase in Android‐related threats, whereas some user‐related factors do not demonstrate statistically significant associations. The results highlight the importance of system security, safe usage practices, user behavior, malware protection, and application‐related security considerations in reducing security risks. The study provides a user‐oriented statistical perspective on Android security and establishes a basis for future research involving technical vulnerability assessment, automated threat detection, and machine‐learning‐based security mechanisms.
Language: English
Open Access status: bronze
OpenAlex ID: W7213786626
FWCI:
Cites: 22
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Related to: 10