Transcript
Page 1: Speak Security: Under the Hood of the OpenDNS Security Research Labs with Dhia and Dima

Umbrella Confidential

1_Title (1)Under the Hood: OpenDNS Security Labs

with Dhia and Dima

Page 2: Speak Security: Under the Hood of the OpenDNS Security Research Labs with Dhia and Dima

Umbrella Confidential

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#2 Apr 8, 2023 Umbrella Confidential

ASIA-PACIFIC

EUROPE, MIDDLE

EAST & AFRICA

AMERICAS

COMPANY BACKGROUND

50M+ ACTIVE USERS DAILY23 DATA CENTER LOCATIONSZERO DOWNTIME, SINCE 2006

50B+ REQUESTS DAILY160+ COUNTRIES W/USERSZERO NET NEW LATENCY

GLOBAL SECURITY NETWORK208.67.220.220 208.67.222.222

Page 3: Speak Security: Under the Hood of the OpenDNS Security Research Labs with Dhia and Dima

Umbrella Confidential

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#3 Apr 8, 2023 Umbrella Confidential

UMBRELLA BY OPENDNS

80M+

REQUESTS TO ADVANCED MALWARE, BOTNET & PHISHING THREATS BLOCKED DAILYNEW THREAT ORIGINS DISCOVERED OR PREDICTED DAILY

100K+

THE ONLY CLOUD-DELIVERED AND DNS-BASED WEB SECURITY SOLUTION

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Umbrella Confidential

1_Title (1)Dhia MahjoubSr. Security ResearcherOpenDNS Labs

Dima KumetsSr. Product ManagerOpenDNS

YOUR PRESENTERS:

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Umbrella Confidential#5 Apr 8, 2023 Umbrella Confidential

AI

HadoopHBASEHIVE,PI

G…Collecti

veDiscov

ery

RESEARCHINNOVATIONS

Graph Theor

y

Pattern

Discovery

Anomaly

Detection

Voting

Trust

RESEARCH

Human

Intel

Payload/

Binaries

100+ sensors

HTTP/HTTPS

20 Data Cente

rs

BIG DATA

50+ Million Users

DNS

160+ countries

50+Billion Querie

s

ANALYZING DATA TO EXTRACT ACTIONABLE SECURITY INFORMATION

Reporting

Security Communit

y

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Changing Threat Landscape

A crime economic model• Distributed specialists• Scalable• Profitable

Examples of players• Vulnerability specialists• Distributors• Malware Writers

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PREDICTIVETHREAT

INTELLIGENCE

Internet-wide pattern analysisPredictive in natureOpenDNS Approach

MALWAREANALYSIS

Sample analysisReactive in nature

Most common approach

VS

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Who is OpenDNS Labs?

Graph Theory Scientists

Visualization Experts

Algorithmists/Programmers

Machine Learning Developers

Predictive Threat Labs

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Process

Goal: Automated, Real-time threat intelligence forprevention and containment

• Analyze huge data sets• Develop model for bad actor behavior• Create classifiers• Validate and test• Combine to detect new threats

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The Big Data

Traffic patterns• Source• Destination• Volume• Time

50B Queries/Day

Example: CryptolockerCo-Occurrence + DGA

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DGA: Domain Generation Algorithm

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DGA: Domain Generation Algorithm

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The Big Data

Passive/Infrastructure• Domain• IP• Prefix+ ASN• Changes

Example: Fast Flux

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Fast Flux

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Fast Flux

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Fast Flux

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Build Classifiers

• What’s a classifier• How does it work• How to test for false positives

• Combining classifiers to productionalize

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LABS.UMBRELLA.COM

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[email protected]

OR JUST TWEET @THINKUMBRELLA


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