ACCUSE: Helping Users to minimize Android App Privacy Concerns
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ACCUSE is a tool that analyzes Android apps and assigns them risk levels related to their permissions and data access. It extracts metadata on over 11,000 apps from the Google Play Store, including permissions, downloads, and ratings. It then clusters apps based on their permissions and calculates three risk levels - normal, dangerous, and system - related to different permission types. ACCUSE also factors in app popularity and ratings to dampen the assigned risk for preloaded apps and highly rated apps. The tool allows analyzing apps with similar functions to see variations in their risk assessments and compares its risk model to others from previous research.
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ACCUSE: Helping Users to minimize Android App Privacy Concerns
- 1. ACCUSE: Helping Users to minimize Android App Privacy Concerns 4th IEEE/ACM International Conference on Mobile Software Engineering and Systems May 22-23, Buenos Aires (Argentina) Presented by : Majda Moussa, Giulio Antoniol, Massimiliano di Penta and Giovanni Beltrame
- 2. ο± Problem Statement: Why it is not like driving a car? User Data = User Developers Apps Approach ConclusionIntroduction Results 2
- 3. ο± ACCUSE Python Script Androguard Apps Clustering Permissions Mapping NRL Risk SYS Risk DGR Risk Play Store APK File Data Extraction Module Data Processing Module Risk Assessment Module Risk Assignment Graph DGR NRL SYS Apps metadata: Descriptions Downloads Ratings Permissions Protection levels ACCUSE: Android Confidentiality Concern User Support Environment ConclusionResultsApproachIntroduction 3
- 4. ο± ACCUSE Python Script Androguard Apps Clustering Permissions Mapping NRL Risk SYS Risk DGR Risk Play Store APK File Data Extraction Module Data Processing Module Risk Assessment Module Risk Assignment Graph DGR NRL SYS Apps metadata: Descriptions Downloads Ratings Permissions Protection levels ACCUSE: Android Confidentiality Concern User Support Environment ConclusionResultsApproachIntroduction 4
- 5. ο± ACCUSE Python Script Androguard Apps Clustering Permissions Mapping NRL Risk SYS Risk DGR Risk Play Store APK File Data Extraction Module Data Processing Module Risk Assessment Module Risk Assignment Graph DGR NRL SYS Apps metadata: Descriptions Downloads Ratings Permissions Protection levels ACCUSE: Android Confidentiality Concern User Support Environment ConclusionResultsApproachIntroduction 5
- 6. ο± ACCUSE Python Script Androguard Apps Clustering Permissions Mapping NRL Risk SYS Risk DGR Risk Play Store APK File Data Extraction Module Data Processing Module Risk Assessment Module Risk Assignment Graph DGR NRL SYS Apps metadata: Descriptions Downloads Ratings Permissions Protection levels ACCUSE: Android Confidentiality Concern User Support Environment ConclusionResultsApproachIntroduction 6
- 7. ο± NaΓ―ve approach App permissions Normal permissions Dangerous permissions System permissions PNRL PDGR PSYS ππ π βΆ ππππ’πππππ πππ‘π ππ π‘βπ ππππππ π πππ ππ π‘βπ πππ’π π‘ππ ππ π΄πππ πΆ. π ππ π π·πΊπ πππ = ΰ· π π π π·πΊπ 1 β ππ π ConclusionResultsApproachIntroduction 7
- 8. ο± Explicit usersβ knowledge π ππ π π·πΊπ πππ = π πΉ β ππΉ β ΰ· π π π π·πΊπ πππ 1 β π π(π π πΉ = 1 β ππ πΉ π π πππππ(πππ) ππΉ = 1 β ππ πΉ π·π(πππ) π·π πππ = πΌππ 255 β log10(π·(πππ)) 6 /255 ConclusionResultsApproachIntroduction 8
- 9. πππ‘ππ π ππ π πππ = π1 β π ππ π ππ πΏ 2 + π2 β π ππ π π·πΊπ 2 + π3 β π ππ π πππ 2 ο± Risk computing ConclusionResultsApproachIntroduction 9
- 10. For the 50 Malware apps: Compute the ranges [min R, max R], [min D, max D]. Generate 10 sub-intervals, out of the computed ranges, and record the percentage (P) of the 50 apps in each subinterval . According to P, compute RF and PF by generating random values for ratings and downloads in the different sub-intervals. β’ Market Dataset: ~ 11 700 apps form Google-Play. β’ Malware Dataset: ~ 900 apps from VirusShare and ~ 50 form Google-Play. ο± Datasets ο± Missed Information for Malware dataset ConclusionResultsApproachIntroduction 10
- 11. ο± To what extent apps with the same functionality exhibit different risk levels using ACCUSE? ConclusionApproach ResultsIntroduction 11
- 12. ο± How does ACCUSE compare with the risk model proposed by Peng et al. [3]? ConclusionApproach ResultsIntroduction 12 ACCUSE (RF and PF belief weights of 100% βw1 = 10, w2 = 100 and w3 =1000) compared to the generative models (BNB, PNB and HMNB).
- 13. ο± How does ACCUSE compare with the risk model proposed by Peng et al. [3]? ConclusionApproach ResultsIntroduction 13 AUC distribution obtained throughout the random generation process of malware apps rating information.
- 14. ο± ACCUSE (Android Confidentiality Concern User Support systEm) allows: ο± Work-in-progress is devoted to: β’ Extend the study to further apps and malware. β’ Assess the ACCUSE usefulness through a user study β’ Implement it as a real-time social media feedback mechanisms. β’ differently weighting the importance of different classes of Android permissions. β’ damping the risk of apps based on their rating and popularity. β’ plotting the risk using heat colors in a three dimensional space (NORMAL, DANGEROUS and SYSTEM risk). ResultsApproach ConclusionIntroduction 14
- 15. Questions?