Security Blog
The latest news and insights from Google on security and safety on the Internet
Identifying Intrusive Mobile Apps Using Peer Group Analysis
12 de julio de 2017
Posted by Martin Pelikan, Giles Hogben, and Ulfar Erlingsson of Google’s Security and Privacy team
Mobile apps entertain and assist us, make it easy to communicate with friends and family, and provide tools ranging from maps to electronic wallets. But these apps could also seek more device information than they need to do their job, such as personal data and sensor data from components, like cameras and GPS trackers.
To protect our users and help developers navigate this complex environment, Google analyzes privacy and security signals for each app in Google Play. We then compare that app to other apps with similar features, known as functional peers. Creating peer groups allows us to calibrate our estimates of users’ expectations and set adequate boundaries of behaviors that may be considered unsafe or intrusive. This process helps detect apps that collect or send sensitive data without a clear need, and makes it easier for users to find apps that provide the right functionality and respect their privacy. For example, most coloring book apps don’t need to know a user’s precise location to function and this can be established by analyzing other coloring book apps. By contrast, mapping and navigation apps need to know a user’s location, and often require GPS sensor access.
One way to create app peer groups is to create a fixed set of categories and then assign each app into one or more categories, such as tools, productivity, and games. However, fixed categories are too coarse and inflexible to capture and track the many distinctions in the rapidly changing set of mobile apps. Manual curation and maintenance of such categories is also a tedious and error-prone task.
To address this, Google developed a machine-learning algorithm for clustering mobile apps with similar capabilities. Our approach uses deep learning of vector embeddings to identify peer groups of apps with similar functionality, using app metadata, such as text descriptions, and user metrics, such as installs. Then peer groups are used to identify anomalous, potentially harmful signals related to privacy and security, from each app’s requested permissions and its observed behaviors. The correlation between different peer groups and their security signals helps different teams at Google decide which apps to promote and determine which apps deserve a more careful look by our security and privacy experts. We also use the result to help app developers improve the privacy and security of their apps.
Apps are split into groups of similar functionality, and in each cluster of similar apps the established baseline is used to find anomalous privacy and security signals.
These techniques build upon earlier ideas, such as using
peer groups
to analyze privacy-related signals,
deep learning for language models
to make those peer groups better, and
automated data analysis
to draw conclusions.
Many teams across Google collaborated to create this algorithm and the surrounding process. Thanks to several, essential team members including Andrew Ahn, Vikas Arora, Hongji Bao, Jun Hong, Nwokedi Idika, Iulia Ion, Suman Jana, Daehwan Kim, Kenny Lim, Jiahui Liu, Sai Teja Peddinti, Sebastian Porst, Gowdy Rajappan, Aaron Rothman, Monir Sharif, Sooel Son, Michael Vrable, and Qiang Yan.
For more information on Google’s efforts to detect and fight potentially harmful apps (PHAs) on Android, see
Google Android Security Team’s Classifications for Potentially Harmful Applications
.
References
S. Jana, Ú. Erlingsson, I. Ion (2015).
Apples and Oranges: Detecting Least-Privilege Violators with Peer Group Analysis
. arXiv:1510.07308 [cs.CR].
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, J. Dean (2013).
Distributed Representations of Words and Phrases and their Compositionality
. Advances in Neural Information Processing Systems 26 (NIPS 2013).
Ú. Erlingsson (2016).
Data-driven software security: Models and methods
. Proceedings of the 29th IEEE Computer Security Foundations Symposium (CSF'16), Lisboa, Portugal.
No hay comentarios :
Publicar un comentario
Etiquetas
#sharethemicincyber
#supplychain #security #opensource
android
android security
android tr
app security
big data
biometrics
blackhat
C++
chrome
chrome enterprise
chrome security
connected devices
CTF
diversity
encryption
federated learning
fuzzing
Gboard
google play
google play protect
hacking
interoperability
iot security
kubernetes
linux kernel
memory safety
Open Source
pha family highlights
pixel
privacy
private compute core
Rowhammer
rust
Security
security rewards program
sigstore
spyware
supply chain
targeted spyware
tensor
Titan M2
VDP
vulnerabilities
workshop
Archive
2024
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2023
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2022
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2021
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2020
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2019
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2018
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2017
dic
nov
oct
sept
jul
jun
may
abr
mar
feb
ene
2016
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2015
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
ene
2014
dic
nov
oct
sept
ago
jul
jun
abr
mar
feb
ene
2013
dic
nov
oct
ago
jun
may
abr
mar
feb
ene
2012
dic
sept
ago
jun
may
abr
mar
feb
ene
2011
dic
nov
oct
sept
ago
jul
jun
may
abr
mar
feb
2010
nov
oct
sept
ago
jul
may
abr
mar
2009
nov
oct
ago
jul
jun
mar
2008
dic
nov
oct
ago
jul
may
feb
2007
nov
oct
sept
jul
jun
may
Feed
Follow @google
Follow
Give us feedback in our
Product Forums
.
![[UNSET] (27).png](https://lh5.googleusercontent.com/CR_TnXcPF5OVJNbGvw6TS6DPuKGuS68wJAEppBZgbISVSgiuWKzrJlUfgSk7oZoJJlpXvBr6tFrcSiJn2hqZYZlcve9RSHgmnuh8hwpr0Yd9idiLdtWktk5qIuO4rzWYdprdZyBU)
Follow
No hay comentarios :
Publicar un comentario