Delegate the smartphone user? Security awareness in smartphone platforms
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TLDR
A prediction model is built to identify users who trust the app repository and is assessed, evaluated and proved to be statistically significant and efficient.About:
This article is published in Computers & Security.The article was published on 2013-05-01 and is currently open access. It has received 238 citations till now. The article focuses on the topics: Cloud computing security & Security through obscurity.read more
Citations
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DroidDetector: Android Malware Characterization and Detection Using Deep Learning
TL;DR: An online deep-learning-based Android malware detection engine (DroidDetector) that can automatically detect whether an app is a malware or not is implemented and shows that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data.
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Determining employee awareness using the Human Aspects of Information Security Questionnaire (HAIS-Q)
TL;DR: Results from 500 Australian employees indicate that knowledge of policy and procedures had a stronger influence on attitude towards policy and procedure than self-reported behaviour, suggesting that training and education will be more effective if it outlines not only what is expected but also provides an understanding of why this is important.
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Evaluating mobile phone applications for health behaviour change: A systematic review:
TL;DR: This review is unable to suggest a single best practice approach to evaluate mobile health apps, but suggests better ways to assess the quality and effectiveness of apps are needed.
Journal ArticleDOI
The Human Aspects of Information Security Questionnaire (HAIS-Q)
Kathryn Parsons,Dragana Calic,Malcolm Robert Pattinson,Marcus A. Butavicius,Agata McCormac,Tara Zwaans +5 more
TL;DR: Results indicated that participants who scored more highly on the HAIS-Q had better performance in the phishing experiment, and provided evidence for its convergent validity, which means the HAis-Q can predict an aspect of information security behaviour, and provides evidence for the validity of this instrument.
The Privacy and Security Behaviors of Smartphone App Developers
TL;DR: It is found that smaller companies are less likely to demonstrate positive privacy and security behaviors and third-party tools for ads and analytics are pervasive, and developers aren’t aware of the data collected by these tools.
References
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