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Fix What Matters
Michael Roytman
SIRAcon October 21, 2013
Why You Should(n’t) Listen

Michael Roytman
• Data Scientist, Risk I/O
• MS Operations Research, Georgia Tech
• Fraud Detection, Large Bank
• Naive Grad Student Not Too Long Ago
• Still Plays With Legos
• Barely Passed Regression Analysis
Roadmap

• The Struggle
• What’s Bad?
• What’s Good?
• Framework
• Data Driven Insights

• Decision-Making
Starting From Scratch

“It is a capital mistake to theorize
before one has data. Insensibly one
begins to twist facts to suit theories,
instead of theories to suit facts.”

-Sir Arthur Conan Doyle, 1887
Starting From Scratch
Starting From Scratch

Primary Sources!
Twitter!
InfoSec Blogs!
Academia!
•  GScholar!
•  JSTOR!
•  IEEE!
•  ProQuest!

•  CISOs
CSIOs!
•  Pen Testers!
•  Threat Reports!
•  SOTI/DBIR!
!

Text

•  Thought Leaders (you
know who you are)!
•  BlackHats!
•  Vuln Researchers!

•  MITRE!
•  OSVDB!
•  NIST CVSS
Committee(s)!
•  Internal Message
Boards for ^!
Data Fundamentalism
Don’t Ignore What a Vulnerability Is: Creation Bias !
(http://blog.risk.io/2013/04/data-fundamentalism/) !

Jerico/Sushidude @ BlackHat !
(https://www.blackhat.com/us-13/briefings.html#Martin)!

Luca Allodi - CVSS DDOS !
(http://disi.unitn.it/~allodi/allodi-12-badgers.pdf):!
Data Fundamentalism - What’s The Big Deal?

!

”Since 2006 Vulnerabilities have declined by 26 percent.” !
(http://csrc.nist.gov/groups/SNS/rbac/documents/vulnerability-trends10.pdf)!
!
!

“The total number of vulnerabilities in 2013 is up 16 percent so far when
compared to what we saw in the same time period in 2012. ”!
(http://www.symantec.com/content/en/us/enterprise/other_resources/b-intelligence_report_06-2013.en-us.pdf)!
!
!
What’s Good?

Bad For Vulnerability Statistics:!
!

NVD, OSVDB, ExploitDB, CVSS, Patches, Microsoft Reports, etc, et al, and so on. !

Good For Vulnerability Statistics:!
!

Vulnerabilities. !
Data Is Everything And Everything Is Data.
What’s Good?
What’s Good?
What’s Good?
What’s Good?
What’s Good?
What’s Good?
Counterterrorism

Known Groups
Past
Incidents,
Close
Calls

Targets,
Layouts

Threat Intel,
Analysts

Surveillance
What’s Good?
Uh, Sports?
Opposing
Teams, Specific
Players
Learning
from
Losing

Roster,
Player
Skills

Scouting
Reports,
Gametape

Gameplay
InfoSec?
Defend Like You’ve Done It Before
Groups,
Motivations

Learning
from
Breaches

Asset
Topology,
Actual Vulns
on System

Vulnerability
Definitions

Exploits
Work With What You’ve Got:
Akamai, Safenet

NVD,
MITRE

ExploitDB,
Metasploit
Add Some Spice
Show Me The Money

23,000,000 Vulnerabilities!
Across 1,000,000 Assets!
Representing 9,500 Companies!
Using 22 Unique Scanners!
Whatchu Know About Dat?(a)
!

Duplication
Vulnerability Density
Remediation
Duplication
2,250,000

2,025,000

1,800,000

1,575,000

1,350,000

1,125,000

900,000

675,000

450,000

225,000

0

2 or more scanners 3 or more

4 or more

5 or more

6 or more
Duplication
We Have: F(Number of Scanners) => Number of Duplicate Vulnerabilities
We Want: F(Number of Scanners) => Vulnerability Coverage

<---------Good Luck!

Make Decisions At The Margins!
100.0

75.0

50.0

25.0

0.0
0

1

2

3

4

5

6
Density
Hostname

Type of Asset
Hostname

1000

IP Address

200,000

File

10,000

Url

IP

20,000

Netbios

Netbios

~Count

5,000

File

Url

0.0

22.5

45.0

67.5

90.0
CVSS And Remediation Metrics
Average Time To Close By Severity

Oldest Vulnerability By Severity

1400.0

1050.0

700.0

350.0

0.0
1

2

3

4

5

6

7

8

9

10
CVSS And Remediation - Lessons From A CISO
Remediation/Lack Thereof, by CVSS

1

2

3

4

5

6

NVD Distribution by CVSS

7

8

9

10
The Kicker - Live Breach Data

1,500,000 !
Vulnerabilities Related to Live Breaches Recorded!
June, July 2013 !
CVSS And Remediation - Nope
Oldest Breached Vulnerability By Severity

7000.0

5250.0

3500.0

1750.0

0.0
1

2

3

4

5

6

7

8

9

10
CVSS - A VERY General Guide For Remediation - Yep
Open Vulns With Breaches Occuring By Severity

160000.0

120000.0

80000.0

40000.0

0.0
1

2

3

4

5

6

7

8

9

10
The One Billion Dollar Question

Probability(You Will Be Breached On A Particular Open Vulnerability)?
=(Open Vulnerabilities | Breaches Occurred On Their CVE)/(Total Open Vulnerabilities)

1.98%
I Love It When You Call Me Big Data
Probability A Vulnerability Having Property X Has Observed Breaches
RANDOM VULN
CVSS 10
CVSS 9
CVSS 8
CVSS 6
CVSS 7
CVSS 5
CVSS 4
Has Patch
0.00000

0.01000

0.02000

0.03000

0.04000
What’s the Alternative?
I Love It When You Call Me Big Data
Probability A Vulnerability Having Property X Has Observed Breaches
Random Vuln

CVSS 10

Exploit DB

Metasploit

MSP+EDB

0.0

0.1

0.2

0.2

0.3
Data Is Everything And Everything Is Data.
Be Better Than The Gap
I Love It When You Call Me Big Data

Spray and Pray => 2%
!

CVSS 10 => 4%
!

Metasploit + ExploitDB => 30%
!

A Good Model That’s Not Built By One Kid
Without Hadoop => ???!
Thank You

Don’t Be A Stranger
Blog: http://blog.risk.io
Twitter: @mroytman

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