English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper focuses on mobile apps serving the underground economy byproviding illegal services in the mobile system (e.g., gambling, porn, scam).These apps are named as underground economy apps, or UEware for short. As mostUEware do not have malicious payloads, traditional malware detection approachesare ineffective to perform the detection. To address this problem, we propose anovel approach to effectively and efficiently detect UEware by considering thetransition orders of the user interfaces (UIs), which determine the usagescenarios of these apps. Based on the proposed approach, we design a systemnamed DeUEDroid to detect the UEware via scene graph. To evaluate DeUEDroid, wecollect 26, 591 apps to evaluate DeUEDroid and build up the first large-scaleground-truth UEware dataset (1, 720 underground economy apps and 831 legitimateapps). The evaluation result shows that DeUEDroid can construct scene graphaccurately, and achieve the accuracy scores of 77.70% on thefive-classification task (i.e., gambling game, porn, financial scam,miscellaneous, and legitimate apps), reaching obvious improvements over theSOTA approaches. Running further on 24, 017 apps, DeUEDroid performs well inthe real-world scenario to mitigate the threat. Specifically, by usingDeUEDroid, we found that UEware are prevalent, i.e., 61% apps in the wild and21% apps in the app stores are UEware (with over 72% accuracy after the manualinvestigation). We will release our dataset and system to engage the communityafter been accepted.

Illegal But Not Malware: An Underground Economy App Detection System Based on Usage Scenario