Big Data - Introduction
High volume, velocity and variety
information assets that demand cost-
effective, innovative and reliable forms of
information processing for enhanced
insight and decision making
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Big Data – Introduction Cont.
• Variety – Big data is any type of data: structured and unstructured data such as text, sensor data, audio, video, click
streams, log files and more. New insights are found when
analyzing these data types together
• Volume – Enterprises are awash with ever-growing data of all types, easily amassing terabytes even petabytes of information
• Velocity – For time-sensitive processes such as catching fraud, big data must be used as it streams into your
enterprise in order to maximize its value
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Security Trends & Challenges Up to date organizations confront unprecedented security arising mainly from:
risks
1. Mobility, dissolves
and the “consumerization” of enterprise IT network boundaries
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Security Trends & Challenges Cont. 2. Highly
skilled, attacks
sophisticated, non signature targeted cyber
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Security Trends & Challenges Cont.
circumvent traditional security systems
requires organizations to reinvent their
security approach
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The dissolution of traditional defensive
perimeters coupled with attackers ability to
Big Data & Intelligence Driven Security Big Data fuels intelligence driven security – • Big data encompasses the breadth of sources and the
information depth needed to:
1)
2)
3)
Assess risks
Detect illicit activities and advanced cyber threats
Allow advanced predictive capabilities and automated RT controls
4)
5)
Serve cyber incident response & investigation services
Deliver compliance
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©2013 AKAMA FASTER FORWARDTM
Big Data & Intelligence Driven Security Use Case
Akamai Confidential Faster ForwardTM ©2012 Akamai I |
Use case – Web User Identity & Big Data
The Goal –
• Verify web customer identity The Process – • Generate, maintain and store a precise continuously evaluated
digital fingerprint of every web customer, based on behavioral
monitoring combined with other "biometrics" measurements
The Means – •
•
•
Ongoing active & passive user activity data feeds 3rd party intelligence (reputation, fraud etc.) Big data platform
14 ©2013 AKAMAI | FASTER FORWARDTM
Big Da Store &
User rofilePs
ocess ed DCa oammo Profil s
cess
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ata Extrac ed D
Use case – User Identity & Big Data
Data
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Access Patterns
LocationP Activity Patterns
Access Patterns
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Preconfigured Users Profile Correlation
Rules
Preconfigured Data Rules Correlation
Preconfigured Data Rules
Preconfigured Deviation Rules ta – Pro
Source IP & NW
Activity Time
Activity Type
Geo Location
Host ID
Reputation Rank
Fraud Rank
Device Fingerprint t
3rd Party Reputation
Data
3rd Party Fraud Data
3rd Party/ MSSPs
Data
Web Server Data
Mobile Operators
Data
DNS Log Data al F l F
15 15 ©2013 AKAMAI | FASTER FORWARDTM
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©2013 AKAMA FASTER FORWARDTM
From Big Data to Big Insights – Best Practice Guidelines
Akamai Confidential Faster ForwardTM ©2012 Akamai I I
From Big Data to Big Insights – Best Practice Guidelines 1) 2)
3)
Define your objectives Understand the potential data feeds needed to meet the objectives
Understand the process needed to obtain, format correctly, clean and
standardize
Assess the platform and infrastructure needed to obtain, process,
manage and use the data
Start small
4)
5) 6) 7)
Assure data is safe and private Be transparent about data practices
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