(ISC)² Security Congress APAC 2017

2. Insider Threats Detection in
Cloud using UEBA
Cloud Threat Analyzer
Lucas Ko, Taiwan
lucasko@iii.org.tw
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3. Lucas Ko
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» National Taiwan University of Science and Technology
• Master’s degree in Computer Science
» Institute for Information Industry
• Project Manager
• Responsibilities：
- Penetration test of bank, gov
- Detection system development
- Log analysis
» Zero-day Finder, such as : CVE-2017-5481

4. Outline
» Insider Threats in Cloud
» User and Entity Behavior Analytics
» Anomaly Detection System
» Case Study
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5. Insider Threats in Cloud
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6. Data Stored in the Cloud
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44% Email
32% Customer Data
31%
30% Employee Data
26%
Sales &
Marketing Data
Contract, Invoices,
Orders
[1] “Cloud Security 2016 Spotlight Report”, CloudPassage

7. Cloud Management is Hard
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Work
at home
Mobile
Google Drive
insecure
Company
Office 365
» Cloud storage is more convenient, but
risk.
• Insider could easily collect data who is
not in company.

8. Security Threats in Cloud
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53% 44% 39% 33%
Unauthorized
Access
Hijacking of
Accounts
Insecure
Interfaces/APIs
External Sharing
of Data
[1] “Cloud Security 2016 Spotlight Report”, CloudPassage

9. Most concerned about insider threats
9
71%
Inadvertent
data leak
[2] “Insider Threat 2016 Spotlight Report”
68%
Ignore
policy
61%
Malicious
data leak

10. How to protect cloud data from
insider threats?
User and Entity Behavior Analytics
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11. 11
What is UEBA ?
» Expands the definition from UBA. Such as
devices, applications, data, anything
» Integrates machine learning, behavior of
user and entity features
» UEBA is used for insider threat detection.

12. How UEBA protected us ?
12
User Behaviors
Entity Features
Log
Collection
Machine
Learning
Risk
Assessment
Behavior
Model
Clustering
Predict
Anomalies
Risk
Estimator

13. 13
Detection is based on UEBA
» Integrated recommendation system
( collaborative filtering ) with entity.
• Directory Tree Structure
Users access logs
Detect Insider
Threats
Collaborative filtering from
file access behaviors
Drive file proximity score
measuring
Past access
behavior matrix
Structure-oriented risk
propagation
Current access
behavior vector

14. Anomaly Detection System
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15. Anomaly Detection System
» Using recommendation system to detect
abnormal behavior.
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Users
Items
Highly Recommend
Not Recommended
（Abnormal Behavior）
Access Matrix
(Binary Matrix)
Recommendation System
Recommend additional items with similar properties.

16. Abnormal Access Behavior
» Detected cross-group abnormal access behavior.
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Cross-group
abnormal access
A team’s files
Similar user
Read Recommend
File
B team’s files
Similar user
Read Recommend
File
Recommendation
System
Detect

17. 17
Recommendation System is not
great at all
» Cold Start Problem：
• There is no access behavior in past for
new file.
- Can not predict for current behaviors of
new file.
Users
Item
Access Matrix Cold Start Problem
New file log
Insiders New file

18. Integrated Recommendation System
with entity
» Directory Tree Structure
• Members in the the same project
will have similar behavior and
directory tree structure.
• Averages the nearby similarity
scores to be the new file’s
similarity score .
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Old files
Highly Similarity Score
New files

19. Steps in the Anomaly Detection
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Log Collection Model Risk Assessment
» Google Drive
• Access Log
• Directory Tree
Structure
» 150 employees
» Last 6 months logs
» Collaborative
Filtering
• Alternating Least
Squares
» Risk Estimator
• File proximity
score measuring.

20. Log Collection
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Log Collection Model Risk Assessment
» Collected access logs from Google Drive

21. Types of Access Log
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Log Collection Model Risk Assessment
change_acl_editors
ACL
Change
Create Trash Remove from folder
Edit View Add to folder
Delete Download Preview rename
Move
Upload
Pint
Access
change_doc_access_scope
change_doc_visbility
change_user_access

22. Data Pre-Processing
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Log Collection Model Risk Assessment
Google Drive
Access Matrix
» Access Log
» Directory Tree
Structure
Directory Tree Structure

23. To Solved the Sparse Problem
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Log Collection Model Risk Assessment
Users
Files
Users
Directories
11%
89%
Directories Files
» Accessed different files in the same directory
• They were considered to be the same
behavior.
» According to our statistics:
» Files account for 89% .
» Directories account for 11%

24. Types of Recommendation System
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Log Collection Model Risk Assessment
Recommendation
System
Content-based
Hybrid-based
(CF + Cotent)
Model-based
Collaborative
Filtering
Neighborhood
-based
User-based
Item-based

25. Collaborative Filtering
» A method of making predictions
about the interests of a user by
collecting preferences.
» Types of preference
• Explicit：Users rate for items.
• Implicit：Observation of user’s
behavior.
- Access Behavior on file.
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26. Alternating Least Squares
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Log Collection Model Risk Assessment
Users
Items
Users
latent factors
Item
latent
factors
U IR
» ALS Features：
• Model-based.
• Easy to parallelize.
• Quick to converge.
» Steps：
• Start with random U & I matrix.
• Optimize user vectors based on files.
• Optimize file vectors based on users.
• Repeat until converged.
Prediction

27. Entity
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» File proximity score measuring.
» User and Entity Behavior
Analytics
• Structure-oriented risk
propagation.
» Measuring Strategy：
• Straight parents.
• All children.
A
B C
D E F
G H
I J
K L M
Log Collection Model Risk Assessment

28. Risk Estimator
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Log Collection Model Risk Assessment
» Reversed score from prediction
• High score means more risk.

29. Case Study
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30. Case：Cross-Group Access
» File link was shared in communication APP.
• Every one clicked link from APP.
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Insi
er
File Link
Administrator
click
click
Shared
APP

31. Case：High-risk Employee
» We found out the high-risk employee who collects
data before quitting job.
» Collected other teams’ documents on Google Drive.
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Team A Team B
Abnornal
Access

32. Case：Shared Account
» Using shared account to access the file
document. (Privileged account abused)
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Shared Account Employee B
No
permission
Frequently used file Infrequently used file
Login to
shared
account
Abnornal
Access

33. Case：Compromised Account
» A account was compromised
• collected documents in secret.
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Compromised
Account
Abnornal
Access
Insi
er Hacker
Frequently used file Infrequently used file
Login to
compromised
account

34. CloudOrion：Cloud Threat Analyzer
» Demo Site
• https://cloudorion.cyber00rn.org/
» Logs Collection
• Google Drive Audit
» One-Click Authorization
• Google Sign in
» Find Out Insider Threats
• Abnormal behavior detection
» File Permission Management
• Remove all permissions at once
» Third-Party Apps Management
• Identify high-risk applications
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Google Drive
Insider
G Suite
Administrator
NotificationAbnormal
Behavior
Logs Collection

35. Thanks for your listening
Institute for Information Industry
Lucas Ko
lucasko@iii.org.tw
lucasko.tw@gmail.com
CloudOrion：Cloud Threat Analyzer
https://cloudorion.cyber00rn.org/
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