Blog
The latest news from Google AI
App Discovery with Google Play, Part 3: Machine Learning to Fight Spam and Abuse at Scale
Monday, January 30, 2017
Posted by Hsu-Chieh Lee, Xing Chen, Software Engineers, and Qian An, Analyst
In
Part 1
and
Part 2
of this series on app discovery, we discussed using machine learning to gain a deeper understanding of the topics associated with an app, and a deep learning framework to provide personalized recommendations. In this post, we discuss a machine learning approach to fight spam and abuse on apps section of the Google Play Store, making it a safe and trusted app platform for more than a billion Android users.
With apps becoming an increasingly important part of people’s professional and personal lives, we realize that it is critical to make sure that 1) the apps found on Google Play are safe, and 2) the information presented to you about the apps is both authentic and unbiased. With more than 1 million apps in our catalog, and a significant number of new apps introduced everyday, we needed to develop scalable methods to identify bad actors accurately and swiftly. To tackle this problem, we take a two-pronged approach, both employing various machine learning techniques to help fight against spam and abuse at scale.
Identifying and blocking ‘bad’ apps from entering Google Play platform
As mentioned in
Google Play Developer Policy
, we don’t allow listing of malicious, offensive, or illegal apps. Despite such policy, there are always a small number of bad actors who attempt to publish apps that prey on users. Finding the apps that violate our policy among the vast app catalog is not a trivial problem, especially when there are tens of thousands of apps being submitted each day. This is why we embraced machine learning techniques in assessing policy violations and potential risks an app may pose to its potential users.
We use various techniques such as text analysis with word embedding with large probabilistic networks, image understanding with Google Brain, and static and dynamic analysis of the APK binary. These individual techniques are aimed to detect specific violations (e.g., restricted content, privacy and security, intellectual property, user deception), in a more systematic and reliable way compared to manual reviews. Apps that are flagged by our algorithms either gets sent back to the developers for addressing the detected issues, or are ‘quarantined’ until we can verify its safety and/or clears it of potential violations. Because of this app review process combining analyses by human experts and algorithms, developers can take necessary actions (e.g., iterate or publish) within a few hours of app submission.
Visualization of word embedding of samples of offensive content policy violating apps (red dots) and policy compliant apps (green dots), visualized with t-SNE (
t-Distributed Stochastic Neighbor Embedding
).
Preventing manipulation of app ratings and rankings
While an app may itself be legitimate, some bad actors may attempt to create fake engagements in order to manipulate an app’s ratings and rankings. In order to provide our users with an accurate reflection of the app’s perceived quality, we work to nullify these attempts. However, as we place countermeasures against these efforts, the actors behind the manipulation attempts change and adapt their behaviors to bypass our countermeasures thereby presenting us with an
adversarial problem
.
As such, instead of using a conventional supervised learning approach (as we did in the ‘
Part 1
’ or ‘
Part 2
’ of this series, which are more ‘stationary’ problems), we needed to develop a repeatable process that allowed us the same (if not more) agility that bad actors have. We achieved this by using a hybrid strategy that utilizes
unsupervised learning
techniques to generate training data which in turn feeds into a model built on traditional
supervised learning
techniques.
Utilizing data on interactions, transactions, and behaviors occurring on the Google Play platform, we apply anomaly detection techniques to identify apps that are targeted by fake engagements. For example, a suspected app may have all its engagement originating from a single data center, whereas an app with organic engagement will have its engagement originating from a healthy distribution of sources.
We then use these apps to isolate actors who collude or orchestrate to manipulate ratings and rankings, who in turn are used as training data to build a model that identifies similar actors. This model, built using supervised learning techniques, is then used to expand coverage and nullify fake engagements across Google Play Apps platform.
A visualization of how a model trained on known bad actors (red) expands coverage to detect similar bad actors (orange) while ignoring organic users (blue).
We strive to make Google Play the best platform for both developers and users, by enabling fast publication while not compromising user safety. The machine learning capabilities mentioned above helped us achieve both, and we’ll continue to innovate on these techniques to ensure we keep our users safe from spam and abuse.
Acknowledgements
This work was done in close collaboration with Yang Zhang, Zhikun Wang, Gengxin Miao, Liam MacDermed, Dev Manuel, Daniel Peddity, Yi Li, Kazushi Nagayama, Sid Chahar, and Xinyu Cheng.
Labels
2018
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2019
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
