The ISC International Journal of Information Security

Abstract Mobile phones are among the most significant technological advancements, offering unmatched convenience and seamlessly integrating into modern lifestyles. However, their widespread use also facilitates both beneficial and harmful practices. The absence of comprehensive datasets with reliable app descriptions undermines user confidence in Google Play as a trustworthy platform for software. To address this gap, we introduce a new dataset, GP-FACL, in this study. This dataset contains "fake apps” that make false claims in their descriptions and pretend to offer features that do not actually exist. Applications were first manually collected, after which keywords were extracted to generate 2-gram key phrases. These key phrases were then used to automate the collection of additional applications. The final dataset provides a systematic approach for identifying false-claim applications across a variety of app categories. Our approach resulted in 117 applications being verified as containing erroneous or misleading claims. This dataset offers researchers and practitioners a valuable resource for advancing fraud detection and mitigating deceptive applications on mobile platforms.