Reducing Mean Time to Know

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Slides from the webinar led by Ely Kahn and Luis Maldonado discussing strategies to reduce Mean Time to Know in detecting cybersecurity attacks, threats, or data breaches.

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Securely explore your data
SQRRL WEBINAR
Reducing “Mean Time to Know”

© 2015 Sqrrl | All Rights Reserved 2
YOUR WEBINAR HOSTS
•  Sqrrl cofounder / VP Business Development
•  Former Director of Cybersecurity at the
National Security Council Staff / White House
•  Degrees from Wharton and Harvard
•  Sqrrl VP Products
•  Former Director of Product Management at
Vertica, Imprivata, and DataSynapse
•  CS degree from MIT

© 2015 Sqrrl | All Rights Reserved
From securing the country to securing your enterprise
SQRRL HISTORY
Google’s
BigTable
Paper
2006
NSA Builds
Accumulo
2008
Sqrrl
Founded
2012
Sqrrl
Enterprise
1.0
2013
Sqrrl
Enterprise
2.0
2015
Investors: Patented Technology:
3

© 2015 Sqrrl | All Rights Reserved
Sqrrl’s focus today is on Detection and Analysis (i.e., cybersecurity
investigations)
INCIDENT RESPONSE LIFECYCLE
4
Source: NIST

© 2015 Sqrrl | All Rights Reserved 5
CYBERSECURITY INVESTIGATIONS TAXONOMY
Cybersecurity
Investigations
Detection Analysis
Hunting /
IOCs
Threat
Intelligence
Alerting
Alert
Resolution
Incident
Triage
Root Cause /
Forensics
Rule-Based Algorithmic

© 2015 Sqrrl | All Rights Reserved
How do we decrease Mean Time To Know?
MEAN TIME TO KNOW
Mean Time To Identify (MTTI): Detect
than an incident has occurred
Mean Time To Know (MTTK):
Understand root cause of an incident
25%
75%
MTTK
MTTI
% Time Spent on MTTI vs. MTTK
Source: Ponemon Institute
6

© 2015 Sqrrl | All Rights Reserved 8
TOP 5 WAYS TO REDUCE MTTK
1.  Big Data
2.  Linked Data Visualization
3.  Graph Exploration
4.  Investigation Workflow
5.  Advanced Analytics

© 2015 Sqrrl | All Rights Reserved 9
#1 BIG DATA
Current solutions can’t
easily handle the variety
and volume of data that
security analysts need
Volume and Variety of Data

© 2015 Sqrrl | All Rights Reserved 10
Performance Measures
#1 BIG DATA
Source: http://www.pdl.cmu.edu/SDI/2013/slides/
big_graph_nsa_rd_2013_56002v1.pdf
Source: http://arxiv.org/pdf/1406.4923v1.pdf
•  Sqrrl indexes and
stores 25,000 events
per second per node
•  Sqrrl’s core has
proven near-linear
scalability to 2000+
nodes
•  Clustered support for
processing Trillions
of events per day
Data Source Record Count
Ne#low
2,109,409,060

Cisco
ASA
Firewall
2,982,124,483

Websense
924,819,607

MsDns
503,237,033

IsaFw
207,834,546

IIS
38,941,968

Damballa
16,060

Apache
Webserver
5,615,832

ISE
671,006

Radius
1,138,001

Windows
Events
12,220,081

Symantec
EP
1,040,871

FireEye
4,305

Total
Records
6,787,072,853

Node
*
Seconds

271,800

Records/Second/Node

24,971

© 2015 Sqrrl | All Rights Reserved 11
#2 LINKED DATA VISUALIZATION
LOGS
VS.
LINKED DATA

© 2015 Sqrrl | All Rights Reserved
LINKED DATA
•  Organizes data into entities
and relationships (links)
•  More intuitive visualization
•  Surfaces meaning & context
•  Enables faster analysis
12

© 2015 Sqrrl | All Rights Reserved 13
LINKED DATA VISUALIZATION DEMO

© 2015 Sqrrl | All Rights Reserved 14
Pattern Discovery and Matching
#3 GRAPH EXPLORATION
•  Hunting for known patterns
•  Search for the HTTP transaction “triangle”
•  Locate specific instance quickly amongst large volume of transactions

© 2015 Sqrrl | All Rights Reserved 15
GRAPH EXPLORATION DEMO

© 2015 Sqrrl | All Rights Reserved 16
It is easy to get lost in a maze of searches during an investigation
#4 INVESTIGATION WORKFLOW

© 2015 Sqrrl | All Rights Reserved 17
INVESTIGATION WORKFLOW DEMO

© 2015 Sqrrl | All Rights Reserved 18
#5 ADVANCED ANALYTICS
Peer Group
Outlier
Algorithmic approaches to anomaly detection

© 2015 Sqrrl | All Rights Reserved 19
ADVANCED ANALYTICS DEMO

© 2015 Sqrrl | All Rights Reserved 20
www.sqrrl.com
HOW TO LEARN MORE?
• Read our white paper or product paper
• Schedule a demo or proof of concept
• Request a VM or evaluation software

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Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

harshavardhanraghave

Software Quality Assurance Interview Questions

Arshad QA

Reducing Mean Time to Know

1. Securely explore your data SQRRL WEBINAR Reducing “Mean Time to Know”

2. © 2015 Sqrrl | All Rights Reserved 2 YOUR WEBINAR HOSTS •  Sqrrl cofounder / VP Business Development •  Former Director of Cybersecurity at the National Security Council Staff / White House •  Degrees from Wharton and Harvard •  Sqrrl VP Products •  Former Director of Product Management at Vertica, Imprivata, and DataSynapse •  CS degree from MIT

3. © 2015 Sqrrl | All Rights Reserved From securing the country to securing your enterprise SQRRL HISTORY Google’s BigTable Paper 2006 NSA Builds Accumulo 2008 Sqrrl Founded 2012 Sqrrl Enterprise 1.0 2013 Sqrrl Enterprise 2.0 2015 Investors: Patented Technology: 3

5. © 2015 Sqrrl | All Rights Reserved 5 CYBERSECURITY INVESTIGATIONS TAXONOMY Cybersecurity Investigations Detection Analysis Hunting / IOCs Threat Intelligence Alerting Alert Resolution Incident Triage Root Cause / Forensics Rule-Based Algorithmic

6. © 2015 Sqrrl | All Rights Reserved How do we decrease Mean Time To Know? MEAN TIME TO KNOW Mean Time To Identify (MTTI): Detect than an incident has occurred Mean Time To Know (MTTK): Understand root cause of an incident 25% 75% MTTK MTTI % Time Spent on MTTI vs. MTTK Source: Ponemon Institute 6

7. © 2015 Sqrrl | All Rights Reserved Sqrrl MTTK Case Study Large Telecommunications Company Results Challenge Sqrrl Solution Ensured compliance with data security regulations Reduce investigation time from days/weeks to minutes Visibility across more data than previously possible Analyzing more than 1 year of multi-structured security data including for Advanced Persistent (APT), fraud, and insider threats •  Aggregate and store all data •  Gather and profile employee and device behaviors •  Search, query and analyze behaviors, details and anomalies 7

10. © 2015 Sqrrl | All Rights Reserved 10 Performance Measures #1 BIG DATA Source: http://www.pdl.cmu.edu/SDI/2013/slides/ big_graph_nsa_rd_2013_56002v1.pdf Source: http://arxiv.org/pdf/1406.4923v1.pdf •  Sqrrl indexes and stores 25,000 events per second per node •  Sqrrl’s core has proven near-linear scalability to 2000+ nodes •  Clustered support for processing Trillions of events per day Data Source Record Count Ne#low 2,109,409,060 Cisco ASA Firewall 2,982,124,483 Websense 924,819,607 MsDns 503,237,033 IsaFw 207,834,546 IIS 38,941,968 Damballa 16,060 Apache Webserver 5,615,832 ISE 671,006 Radius 1,138,001 Windows Events 12,220,081 Symantec EP 1,040,871 FireEye 4,305 Total Records 6,787,072,853 Node * Seconds 271,800 Records/Second/Node 24,971

14. © 2015 Sqrrl | All Rights Reserved 14 Pattern Discovery and Matching #3 GRAPH EXPLORATION •  Hunting for known patterns •  Search for the HTTP transaction “triangle” •  Locate specific instance quickly amongst large volume of transactions

Reducing Mean Time to Know

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