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Cyber Analytics – A Proactive Approach to Cyber Attacks 1:30 p.m. -2:10 p.m. Speakers include: • Jeff Kidwell, Big Data Federal Sales Leader, IBM • Wayne Wheeles, Cybersecurity Network Forensics Analytic Developer, Six 3 Systems

Cyber Analytics – A Proactive Approach to Cyber Attacks

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Page 1: Cyber Analytics – A Proactive Approach to Cyber Attacks

Cyber Analytics – A Proactive Approach to Cyber Attacks1:30 p.m. -2:10 p.m.

Speakers include:• Jeff Kidwell, Big Data Federal Sales Leader, IBM• Wayne Wheeles, Cybersecurity Network Forensics Analytic Developer, Six 3 Systems

Page 2: Cyber Analytics – A Proactive Approach to Cyber Attacks

Cyber Analytics – A Proactive Approach to Cyber Attacks

Wayne WheelesCyber Security Network Forensics Analytic DeveloperSix 3 Systems

Jeff KidwellBig Data Federal ExecutiveIBM

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About Us

Wayne Wheeles

Developer for Six3 Systems, Cyber and Enterprise Systems Group

Network Forensics, Analytic Developer (CND-OPS)

24 analytics in production, 34 forms of enrichment

Managing Contributor/Committer of SHERPA Project

Jeff Kidwell

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AGENDA

1

2 Cybersecurity Capabilities

2

3

Key Challenges : V T

4 Proactive Approach to Cyber Attacks : Practical Cyber

5

3

Current : Cyber Security Old School Approach

1 IBM Big Data Strategy : Move Analytics to the Data

Conclusions : Questions and Discussion

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Meeting Today’s Analytic Challenges Means Thinking Differently about Architecture

• Data systems optimized for analytic workloads

• Support for different types of analytics

• Simplicity as a design requirement

• Agility as a deployment requirement

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BI / Reportin

g

BI / Reporting

Exploration / Visualization

FunctionalApp

IndustryApp

Predictive Analytics

Content Analytics

Analytic Applications

IBM Big Data Platform

Systems Management

Application Development

Visualization & Discovery

Accelerators

Information Integration & Governance

HadoopSystem

Stream Computing

DataWarehouse

New analytic applications drive the requirements for a big data platform

• Integrate and manage the full variety, velocity and volume of data

• Apply advanced analytics to information in its native form

• Visualize all available data for ad-hoc analysis

• Development environment for building new analytic applications

• Workload optimization and scheduling

• Security and Governance

IBM Big Data Strategy … Move Analytics to the Data

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7Six3 Systems, www.six3systems.com

root ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) sshserve ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) sshserve ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) bin ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) user0 ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) user0 ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) joyko ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) joyko ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:10 - 14:10 (00:00) svn ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) svn ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) nagios ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) nagios ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) lday ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) lday ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) michael ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) michael ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) paulb ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) paulb ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:09 - 14:09 (00:00) root ssh:notty 59.**.**.* Sat Oct 13 14:08 - 14:08 (00:00) PruncuTz ssh:notty 59.**.**.* Sat Oct 13 14:08 - 14:08 (00:00) …… (two attacking servers 45000 hits )

SSH brute force assault on port 22, using a Common password list, (45000 times on one Saturday)

The Cyber Threat: Traditional Threat

Country: China

State/Region: Jiangxi

City: Nanchang

Latitude: 28.55

Longitude: 115.9333

According to statistics supplied to the commission by San Francisco-based service provider CloudFlare Inc., attacks account for about 15 percent of global internet traffic on any given day. That “plummeted to about 6.5 percent” around Oct. 1, 2011, China’s National Day, “when many workers take leave,” according to the draft report.

Coincidence to ponder ?

Source: http://sg.entertainment.yahoo.com/news/slightly-suspicious-worldwide-hack-attacks-152725959.html

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The Cyber Threat: Real World Incident ** DISCLAIMERS **

- The IP Addresses have been changed so please do not look them up.- This is not a government network.- This is an actual real world event on a real network using a honeypot.

What are we going to see?

Stage 1 - The surveillance of the target server to determine attack surfaceStage 2 – Infection of the target serverStage 3 – Data theft from the target serverStage 4 – Beaconing, p0wn3d -> target server is under their control

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The Cyber Threat: Real World Incident

In a world so full of slides, articles, powerpoint-ware and that is awash in a sea of whitepapers….

internet

Router/firewall

IDSNetflow

Honeypot:Bueller

192.168.10.155

Honeypot Network

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Stage 1: Surveillance of the Target Server Attacking IP Address: 162.198.10.15(1 of 16)

Victim IP Address: 192.168.10.155

Scanning Ports: 32772 – 60986 (Y Axis below)

Timeframe: 3 months

Total Ports Evaluated: 1439 Scan Types: 6 distinct classes of scan

Scanning Networks: 5 different Class A Scanning Servers: 16 different servers

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Stage 2: Infection of the Target Server

Country Attacker Victim Destination Port STIME Packets Byte

NOT US 162.198.10.15 192.168.10.155 36549 2012-09-21 21:25:05 380 557810

Attacking IP Address: 162.198.10.15

Victim IP Address: 192.168.10.155

Delivered Packets: 380

Signature: Precedes Beaconing & Data Theft

Port Accessed: 36549

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COUNTRY_CODE Attacker Victim Source Port STIME Packets Byte

NOT US 162.168.10.155 192.168.10.155 80 9/23/12 9:25 PM 606 31853

NOT US 162.168.10.155 192.168.10.155 80 9/27/12 10:29 PM 211 11298

NOT US 162.168.10.155 192.168.10.155 80 10/13/12 4:25 PM 201 11102

NOT US 162.168.10.155 192.168.10.155 80 12/20/12 9:29 PM 197 10570

NOT US 162.168.10.155 192.168.10.155 80 12/20/12 12:29 AM 182 9791

NOT US 162.168.10.155 192.168.10.155 80 01/14/13 1:29 PM 134 7295

NOT US 162.168.10.155 192.168.10.155 80 01/15/13 9:29 PM 91 5214

NOT US 162.168.10.155 192.168.10.155 80 01/16/13 1:29 AM 75 4377

Attacking IP Address: 162.198.10.15

Victim IP Address: 192.168.10.155 Attempted Extractions: 2025

Extraction Technique: Small Packages

Stage 3: Data Theft from Target Server Timeframe: 6 months

Source Ports: 80

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Redirected C&C : 142.192.10.21

Victim IP Address: 192.168.10.155

Beaconing Interval: hourly

Stage 4: Target Server begins Beaconing

Country Code Attacker Victim Victim Outbound STIME Packets Byte

NOT US 142.192.10.21 192.168.10.155 80 8/9/12 9:40 PM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/9/12 10:40 PM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/9/12 11:40 PM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 12:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 1:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 2:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 3:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 4:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 5:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 6:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 7:40 AM 4 216

NOT US 142.192.10.21 192.168.10.155 80 8/10/12 8:40 AM 4 216

Status: p0wn3d

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Netflow Events: 886,147,719

Seems so Simple: The Rest of the StoryIDS Signatures: 68,014,256

Description Event Result Sets Response Time (s)

Stage 1 – Surveillance 1439/578230

Stage 2 – Infection 3142

Stage 3 – Data Theft 202582

Stage 4 – P0wn3d 42110

IBM Pure Data Powered by Netezza

SO how was that possible? Analytics running on one of the most powerful platforms ever built: IBM Pure Data by Netezza

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The Cyber Threat: CRM for Hackers

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Practical Cyber: “Stop Talking, Start Doing”

Lesson Learned #1 – The Op Tempo of the design/development/delivery effort must match the mission op tempo.

Practical Cyber is Built on five key principles and countless lessons learned derived from over a decade in the Cybersecurity domain

Answering your first question:

Yes, it has been in production

for over 6 years

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630; 33%

252; 13%

1008; 53%

Data Retrieval

Research

Analysis

Analyst Yearly Activity Metrics

Analysis Metrics

47% of time dedicatedTo “getting in position” to

perform analysis

Meanwhile

Global IP traffic has increased eightfold over the past 5 years, and will increase threefold over the next 5 years. Overall, IP traffic will grow at a compound annual growth rate (CAGR) of 29 percent from 2011 to 2016. (source: CISCO)

The number of malicious emails as a proportion of total email traffic increased in 2011. Large companies saw the greatest rise, with 1 in 205.1 emails being identified as malicious for large enterprises with more than 2,500 employees. (1 malicious email every two days on average)

The number of targeted attacks increased dramatically during 2011 from an average of 77 per day in 2010 to 82 per day in 2011. And advanced persistent threats (APTs) attracted more public attention as the result of some well publicized incidents.

Key Principle 1: Humans are not scaling

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Key Principle 2: Threat Rich Environment

THREATS InsiderOutsider

Lesson Learned #2 – The threat environment is rich with both internal and external threats, known and unknown…….

UNCHARACTERIZED ACTIVITY

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Key Principle 3: Handling Data at Scale/Speed

DataLesson Learned #3 – A new era of data solutions must be investigated, data volumes are ever increasing and NO ONE WANTS A RETROSPECTIVE !

3,524,984,302 Packet Records a day

503,569,186 Netflow Records a day

Medium Sized Network

Analysts

Near Real Time SLA

Maximize use of analyticsAnswers in seconds/minutes

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Data

Key Principle 4: All data = Net Situational Awareness

Malware/Application Logs

Network Packet Data

IPS/IDS Data

Lesson Learned #4 – Smash the silos and bring the different forms of data together(PCAP, IDS, SILK, MALWARE) and add enrichment

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Key Principle 5: Metrics are not just important in September/October

Lesson Learned #5 – Instrumentation is a must to collect metrics for ease of reporting daily, monthly, yearly.

• Instrument Everything

Analytics

Enrichment

• How much is stored

• How much is processed

• How much is reported on

• How much is retired

• Report Card on IDS Signatures

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Distilled Down: A Cyber Security Solution Should

So, to start being defensive, I need a data-centric system that is, at a minimum,...

Analytics - NOT THE EASY BUTTON, Analytics help the human scale

Real-Time – Forget minutes, milliseconds matter

Flexible – No monoliths, Platform Neutrality, Deal with Malware identification in the

morning, investigate data theft in the afternoon and management reporting in the

evening on the same platform…..

Powerful – Take on all comers…. Simple queries, statistical analysis, analytics

across massive amounts of data.

Instrumented - Collecting metrics on all data being processed

Simple to use - People are The essential element of defensive cyber!

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TopicReader

IngestionTier

Stream 1IDS

Stream 2Netflow

Stream 3PCAP

Stream 4Web Proxy

Extract TransformLoad

TippingFramework

StorageTier

Near Real (NRTDM)Time Data Mart (90 days)

IBM Pure Data Netezza

AnalyticalTier Data Service

BusinessTier Reports

SituationalAwareness Rollups Business

AdaptersANALYTIC

FRAMEWORK

System Reference Model

Long Term (LTDM)Data Mart (18 months)IBM BIG Insights

Enrichment

Sensors

Anal

ytic

1An

alyti

c 2

Anal

ytic

3

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Conclusion: Take away points 99% Rule….Eliminate where possible mundane “data retrieval/manipulation”

tasks (one analytic = 1315hrs per year)

Analytics are not the easy button…..

IBM has a complete proven solution to deliver this capability

Enrichment !! Make sure data is “prepared” for analysis

Scale! ensure that the platform is up to the task (many billions of records)

Keep it simple, ensure that the platform is mission agnostic; no UBER solution!

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QUESTIONS AND DISCUSSION?

[email protected]

“Imagination is more important than knowledge. For knowledge is limited to all we now know and understand, while imagination embraces the entire world, and all there ever will be to know and understand.”

~Albert Einstein

Thanks, I am honored and humbled to have been invited today

@WayneWheeles

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Six3 Systems

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For Additional Information

Visit the Agile Summit Solution Center for demonstrations of these capabilities.

Ask an IBM Ambassador for additional information (case study, white paper, solution brief, etc.) related to the content shared during this session.

For a follow up discussion, complete the IBM Response Card on the table in front of you.