Raffael Marty, CEO
Big Data Visualization
London
February, 2015

Security. Analytics. Insight.2
• Visualization
• Design Principles
• Dashboards
• SOC Dashboard
• Data Discovery and Exploration
• Data Requirements for Visualization
• Big Data Lake
Overview

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I am Raffy - I do Viz!
IBM Research

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Visualization

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Why Visualization?
the stats ...
http://en.wikipedia.org/wiki/Anscombe%27s_quartet
the data...

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Why Visualization?
http://en.wikipedia.org/wiki/Anscombe%27s_quartet
Human analyst:
• patterndetection
• remembers context
• fantasticintuition
• canpredict

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Visualization To …
Present / Communicate Discover / Explore

Design Principles

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Choosing Visualizations
Objective AudienceData

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• Objective: Find attackers in the network moving laterally
• Defines data needed (netflow, sflow, …)
• maybe restrict to a network segment
• Audience: security analyst, risk team, …
• Informs how to visualize / present data
For Example - Lateral Movement
Recon Weaponize Deliver Exploit Install C2 Act

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• Show comparisons, contrasts,
differences
• Show causality, mechanism,
explanation, systematic structure.
• Show multivariate data; that is,
show more than 1 or 2 variables.
by Edward Tufte
Principals of Analytic Design

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Show Context
42

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is just a number
and means nothing without
context
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Show Context

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Use Numbers To Highlight Most Important Parts of Data
Numbers
Summaries

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Additional information about
objects, such as:
• machine
• roles
• criticality
• location
• owner
• …
• user
• roles
• office location
• …
Add Context
source destination
machine and
user context
machine role
user role

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Traffic Flow Analysis With Context

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http://www.scifiinterfaces.com/
• Black background
• Blue or green colors
• Glow
Aesthetics Matter

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B O R I N G

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Sexier

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• Audience, audience, audience!
• Comprehensive Information (enough context)
• Highlight important data
• Use graphics when appropriate
• Good choice of graphics and design
• Aesthetically pleasing
• Enough information to decide if action is necessary
• No scrolling
• Real-time vs. batch? (Refresh-rates)
• Clear organization
Dashboard Design Principles

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SOC Dashboards

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Mostly Blank

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• Disappears too quickly
• Analysts focus is on their own screens
• SOC dashboard just distracts
• Detailed information not legible
• Put the detailed dashboards on the analysts screens!
Dashboards For Discovery

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• Provide analyst with context
• “What else is going on in the environment right now?”
• Bring Into Focus
• Turn something benign into something interesting
• Disprove
• Turn something interesting into something benign
Use SOC Dashboard For Context
Environment informs detection policies

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Show Comparisons
Current Measure
week prior

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• News feed summary (FS ISAC feeds, mailinglists, threat feeds)
• Monitoring twitter or IRC for certain activity / keywords
• Volumes or metrics (e.g., #firewall blocks, #IDS alerts, #failed transactions)
• Top N metrics:
• Top 10 suspicious users
• Top 10 servers connecting outbound
What To Put on Screens
Provide context to individual security alerts
http://raffy.ch/blog/2015/01/15/dashboards-in-the-security-opartions-center-soc/

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Data Discovery &
Exploration

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Visualize Me Lots (>1TB) of Data

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Information Visualization Mantra
Overview Zoom / Filter Details on Demand
Principle by Ben Shneiderman
• summary / aggregation
• data mining
• signal detection (IDS, behavioral, etc.)

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• Access to data
• Parsed data and data context
• Data architecture for central data access and fast queries
• Application of data mining (how?, what?, scalable, …)
• Visualization tools that support
• Complex visual types (||-coordinates, treemaps,
heat maps, link graphs)
• Linked views
• Data mining (clustering, …)
• Collaboration, information sharing
• Visual analytics workflow
Visualization Challenges

Big Data Lake

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• One central location to store all cyber security data
• “Data collected only once and third party software leveraging it”
• Scalability and interoperability
• More than deploying an off the shelf product from a vendor
• Data use influences both data formats and technologies to store the data
• search, analytics, relationships, and distributed processing
• correlation, and statistical summarization
• What to do with Context? Enrich or join?
• Hard problems:
• Parsing: can you re-parse? Common naming scheme!
• Data store capabilities (search, analytics, distributed processing, etc.)
• Access to data: SQL (even in Hadoop context), how can products access the data?
The Big Data Lake

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Federated Data Access
SIEM
dispatcher
SIEM
connector
SIEM console
Prod A
AD / LDAP
HR
…
IDS
FW
Prod B
DBs
Data Lake
Caveats:
• Dispatcher?
• Standard access to dispatcher /
products enabled
• Data lake technology?
SNMP

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Multiple Data Stores
raw logs
key-value
structured
real-time
processing
(un)-structured data
context
SQL
s
t
o
r
a
g
e
stats
index
queue
distributed
processing
a
c
c
e
s
s
graph
Caveat:
• Need multiple types of
data stores

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Technologies (Example)
raw logs
key-value
(Cassandra)
columnar
(parquet)
real-time
processing
(Spark)
(un)-structured data
context
SQL
(Impala,
SparkSQL)
H
D
F
S
aggregates
index
(ES)
queue
(Kafka)
distributed
processing
(Spark)
a
c
c
e
s
s
graph
(GraphX)
Caveat:
• No out of the box
solution available

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SIEM Integration - Log Management First
SIEM
columnar
or
search engine
or
log management
processing
SIEM
connector
raw logs
SIEM console
SQL or search
interface
processing
filtering
H
D
F
S
e.g., PIG parsing

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Simple SIEM Integration
raw, csv, json
flume
log data
SQL
(Impala,
with SerDe)
H
D
F
S
SIEM
connector
SIEM
Requirement:
• SIEM connector to forward text-
based data to Flume.
SQL interface
Tableau, etc.
SIEM console

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SIEM Integration - Advanced
SIEM
columnar
(parquet)
processing
syslog data
SQL
(Impala,
SparkSQL)
H
D
F
S
index
(ES)
queue
(Kafka)
a
c
c
e
s
s
other data
sources
SIEM
connector
raw logs
SIEM console
SQL and search
interface
Tableau, Kibana, etc.
requires parsing and
formatting in a SIEM
readable format (e.g., CEF)

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What I am Working On
Data Stores Analytics Forensics Models Admin
10.9.79.109 --> 3.16.204.150
10.8.24.80 --> 192.168.148.193
10.8.50.85 --> 192.168.148.193
10.8.48.128 --> 192.168.148.193
10.9.79.6 --> 192.168.148.193
10.9.79.6
10.8.48.128
80
53
8.8.8.8
127.0.0.1
Anomalies
Decomposition
Data
Seasonal
Trend
Anomaly Details
“Hunt” ExplainVisual Search
• Big data backend
• Own visualization engine (Web-based)
• Visualization workflows

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BlackHat Workshop
Visual Analytics -
Delivering Actionable Security
Intelligence
August 1-6 2015, Las Vegas, USA
big data | analytics | visualization

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http://secviz.org
List: secviz.org/mailinglist
Twitter: @secviz
Share, discuss, challenge, and learn about security visualization.
Security Visualization Community

Security. Analytics. Insight.
raffael.marty@pixlcloud.com
http://slideshare.net/zrlram
http://secviz.org and @secviz
Further resources: