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Big data … for security James Salter Hewlett Packard Labs December 3, 2015

Big data ... for security

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Page 1: Big data ... for security

Big data … for securityJames SalterHewlett Packard LabsDecember 3, 2015

Page 2: Big data ... for security

This is what we are dealing with...

2

6 Next generation data centres

300K+Employees andcontractors

A massive IT operation

41K+servers

440K+ PCs deployed

15K+switches

1,500+enterprise routers140+

Windows Domain Controllers

Infrastructure

11.5M+ Internet mails per day sent/received

150K+ mobile devices39M

IP Addresses

1.2M devices

450K mailboxes managed

Connectivity

2.5Bsecurity events logged per day

2K+ managed firewalls

970K+devices scanned for vulnerabilities

450Kend points protected with anti-virus

Security

Page 3: Big data ... for security

Security events data

HPE IT operates ArcSight internallyDeployment 25% larger than any other non-governmental installation by volume

1 2 3 4 5 61

10

100

1000

10000

100000

1000000

Even

ts p

er s

econ

d (lo

garit

hmic

sca

le)

DNS traffic per HPE data centre:– 120,000 events/second– ~64B events/day globally

Routers VPN AntiVirus Active Directory Web Proxy DNS

Page 4: Big data ... for security

64 billionDNS events/day

whitelist/blacklist 99%

4

640 milliongreylisted events

Page 5: Big data ... for security

Collection is just part of the storyAnalytics is where the power comes from

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Correlation

Machine learning

Graph analytics

Anomaly detection

Advanced persistent threats

Data exfiltration

User behaviour analysis/insider threat

Endpoint visibility

Page 6: Big data ... for security

Abuse caseBotnet command and control

Bot DNS server

akaajkajkajd.cn?xisyudnwuxu.ru?dfknwerpbnp.biz?mneyqslgyb.info?cspcicicipisjjew.hu?

C2 Server(mneyqslgyb.info)

Attacker can’t maintain C2 server at IP address for very long.

So it registers a random domain name temporarily.

Bot tries a bunch of random names until it finds one that

resolves.

Page 7: Big data ... for security

AssetAsset

Abuse caseDNS tunneling (via subdomains)

Bot DNS server (Compromised) DNS server

(example.com)

93cc3daf.example.com4fac3215.example.coma86f4221.example.comddee9152.example.com8bd5ff12.example.comd4bb92a1.example.comef409132.example.com1bfa3207.example.com298c5b3a.example.com

Page 8: Big data ... for security

Solution architecture: Overview

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DNS server(s)

DNS packet capture

Whitelist

networktap

DNS queriesand responses

Blacklist

Event logging Correlation and alerting

Real-time processing

Near-time, historical analysis

DNS events:queries and replies

Page 9: Big data ... for security

In use at HPE

Hewlett Packard EnterpriseCyber Defense Center, Palo Alto

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Page 10: Big data ... for security

From Labs … to HPE … to Customers

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Screenshot from HPE DNS Malware Analytics

– HPE DNS Malware Analytics

– Cloud-based managed or self-service analytics with on-premises capture modules

Page 11: Big data ... for security

The next challenges

11

? days ?

5 minutes

24 hours

Increase the correlation time window

Data exfiltration “hidden in the noise”

Exf

il

time

Page 12: Big data ... for security

The next challenges

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Security Events

DNS Outgoing ISP Packets

2.564

660B

illio

ns o

f Eve

nts

Per

Day

0

700

350

Complete packet capture for all outgoing ISP connections

Page 13: Big data ... for security

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Thank [email protected]