Splunk Use Cases Webinar

December 25, 2016 | Author: jpl1 | Category: N/A
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Exploring  ³Big  data´   Security  Analytics:  Use   Cases  and  More  

Dave  Shackleford,  SANS  and  Voodoo  Security   Mark  Seward,  Sr.  Director,  Security  and  Compliance,  Splunk  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org  

Recap:  Webinar  #1     ‡ Security  and  Big  Data:  What's  all   the  Hype  About?   ± Defined  ³Big  data´   ± Core  use  cases   ‡ ‡ ‡ ‡

Incident  Response   Root  cause  analysis   Security  intelligence   KPI  analytics  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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The  Need  for  Security  Intelligence   ‡ More  and  more,  we  need  bigger  data   sets  to  analyze   ‡ IT  operational  data  can  provide   incredibly  useful  context  and   correlation  points   ± DB  information   All Security Security Relevant Relevant Data Data ± App  data   ± OS  data   ‡ SIEM  gives  us  a     lot  of  info,  but  not     SIEM   enough     ‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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³Ok,  Ok,  I¶m  convinced.   Big  data  and  the  big   data  phenomenon  is   real.  So  what  do  I  do   with  it?´  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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The  Big  Data  Powered  Business   ‡ Less  µGut  Feeling¶  ±   More  µEvidence¶  based   decisions  

‡ Seen  as  a  way  to   increase  top  line   revenues  and  reduce   expenses   ‡ New  dependence  on   understanding  the  data   ‡ Hurting  my  business   means  messing  with,   stealing,  or  interrupting   the  flow  of  my  data  and   µchanging¶  my  decisions      

³Epsilon leverages big data and analytics. Revenues increased by 20%.´ IBM ³60% potential increase in operating margins possible with big data. McKinsey and Co. June 2011

The  Need  to  µThink  Differently¶  

Creativity     consists  of       Convergent   and   Divergent   Thinking   6

Big-­data  and  Creative   Security  Thinking     Divergent  Thinking:   ʹ ʹ ʹ

The  Aha  moment  /  Spontaneous  epiphany   Remote  associative  processes   Pattern-­based  thinking    

Convergent  Thinking:   ʹ ʹ

ʹ

ʹ

About  analysis  and  attention   The  act  of  µun-­concealing¶  ±  chiseling  away  at  a   problem   Write  a  symphony  /  poem  /  solve  an  algebraic   equation   Stick  with  a  problem  till  it  µcries  uncle¶   7

Security  Intelligence  Requires:   µThinking  Like  a  Criminal¶   9

9 9 9

µNormal¶  IT   Services  Data  

What¶s  the  modus  operandi  of  the   attacker?   What  are  the  most  critical  data  sets   owned  by  the  business?   What  physical  or  virtual  assets  have   the  data?   What  patterns  of  weak-­signals  in   µnormal¶  IT  activities  would  represent   µabnormal¶  human  or  machine   behaviors?  

Application   Physical   Security   DHCP   Data  

GPS  

AD/LDAP  

Netflow  

DNS  

VPN  

Where  are  my  big  data   experts?   Meet your µnew¶ virtual security team

‡

Traditional  security  folk   SOXV«GRPDLQNQRZOHGJH  

‡

Where  will  µbig  data   experts¶  be  hired  

‡

Constituents  will  be   partners  and  partners   constituents  

‡

µHub  and  Spoke¶  design  

‡

Fosters  data-­driven   decision  making  

Finance Team Legal Department

Finance Team

Business Service Providers

Traditional   Security   Team  

Development IT Operations

Business Line Owners

Finance Team

  ³Lets  define  a  new  thinking   process´  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Big  Data,  Big  Thinking,  New  Process   ‡

What  will  cause  the   business  to  stop   functioning?  

‡

What¶s  normal?  

‡

Data  SMEs  from  the   business  and  security   teams  figure  out  ±  µwhat¶s   normal¶  and  what  would   not  be  normal  

‡

Analysis  options   categorized  with   combinations  of  R/T  and   historic  searches  

‡

Support  for  agile   interpretation  and  iteration    

Adapted  from͞The  "Human  Element"  of  the  Big  Data  Equation͟  by  Steve  Durbin  ISF  ,  CRM   magazine,    November  2012  

  Copyright  ©  2012,  Splunk  Inc.  

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Using  the  Process  -­   Example   The  Steps  

The  Response  

Business  Issue  

Service  degradation  causes  monetary   damage  and  customer  satisfaction   issues.  

Construct  one  of  more   hypothesis  (team  creativity   required)    

Unwanted  bots  can  degrade  service  and   steal  content.  

Gather  data  sources  and   expertise    

What  combinations  of  data  would  be   considered  definitive  evidence?  What   might  be  the  first  signs  of  trouble?  List   all  data  in  which  this  might  be  reflected.  

Determine  the  analysis  to   be  performed  

Determine  the  types  of  data  searches   appropriate    

Interpret  the  results  

Do  the  results  represent  false  positives  of   false  positives  or  false  negatives?  Are   there  good  bots  and  bad  bots?   12  

Copyright  ©  2012,  Splunk  Inc.  

Detecting  Account  Take-­over     ‡

Statistical  analytics  and   thresholds   ‡ ‡ ‡

Behavior  of  logins  and  password   changes  and  resets   Analysis  of  same  IP  ±  multiple   password  resets   Multiple  IPs  -­-­  resetting  the  same   account  

‡

How  many  times  people   change  their  bank  information  

‡

How  many  times  they  change   their  credit  card  information  

‡

Does  the  IP  address  (location)   match  the  browser  language   or  time  zone  

Unknown  Threat  Attack   Pattern  -­-­  Example  

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Attack  Pattern  Modeling  ±   Questions  to  Ask   ‡ Is  this  the  first  time  this  

person  has  received  email   from  the  recipient?    

‡ Is  the  website  in  the  email  on  

a  known  list  of  bad  websites?  

‡ Are  their  changes  to  host  

Host based Analytics

config  files  closely  tied  to  a   website  visit?  

‡ If  so  ±  import  PCAP  and  

Flowdata    

‡ Are  there  DNS  requests  to  

known  bad  sites  or  are  the  IP   addresses  of  the  DNS  URL   request  and  responses  the   same  or  different?  

‡ Monitor  port  and  protocol  

usage  unusual  amounts  or   types  15

Network based Analytics

Is  Big  Data  Changing  Security?     Oh  yeah.   ‡ Zions  Bancorporation  presented  at   RSA  2012  on  how  analytics  would   change  their  security  model   forever   ‡ The  goal?  Actionable,  real-­time   security  intelligence  over   petabytes  of  data.  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Zion  Case  Study:  Components   ‡ Looked  to  drive  deeper  forensics  and   build  complex  stats  models   ± Needed  years  of  data   ‡ Logs  are  still  centralized   ± Using  Hadoop  and  unstructured  data   file  stores   ‡ Storing:   ± DB  logs   ± FW  logs/events   ± Antivirus  logs   ± IDS  logs     ± Wire  ACS  transfers   ± Credit  data  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Even  More  Use  Cases   ‡ Fraud  Detection   ± Patterns  of  user  behavior  vs.  ³other   users´   ‡ Intellectual  Property  Theft   ± Data  access  patterns  over  long   time  periods,  with  many  sources   ‡ Security  Monitoring  Optimization   ± Where  are  best  locations  for   sensors  and  event  monitoring?   ± What  are  best/optimal  data   sources?   ‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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So  What  is  Splunk?  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Splunk  Collects  and  Indexes  Any   Machine  Data                           Customer     Facing  Data   ‡

‡ ‡

Outside  the   Datacenter  

Shopping  cart  data   Online  transaction  data  

Logfiles  

Windows  

‡ ‡ ‡ ‡

‡ ‡ ‡ ‡ ‡

Click-­‐stream  data  

Registry   Event  logs   File  system   sysinternals  

Copyright  ©  2011,  Splunk  Inc.  

Configs  

Linux/Unix  

‡ ‡ ‡ ‡

Configurations   syslog   File  system   ps,  iostat,  top  

Messages  

Traps      Alerts  

Virtualization     &  Cloud  

‡ ‡ ‡

Hypervisor   Guest  OS,  Apps   Cloud  

Metrics  

Scripts  

Changes  

Applications  

‡ ‡ ‡ ‡

Web  logs   Log4J,  JMS,  JMX   .NET  events   Code  and  scripts  

DĂŶƵĨĂĐƚƵƌŝŶŐ͕ůŽŐŝƐƚŝĐƐ͙ CDRs  &  IPDRs   Power  consumption   RFID  data   GPS  data  

Tickets  

Databases  

Networking  

‡ ‡ ‡ ‡

‡ ‡ ‡ ‡

Configurations   Audit/query  logs   Tables   Schemas  

Listen  to  your  data.  

Configurations   syslog   SNMP   netflow  

So  What  is  Splunk?  

+   Text Based Search

Statistical Analysis

Time  Index  Ingestion   Text  Base  Search   Nested  Search   Cross  Data-­‐type   Search   cApend   Abstract   Cluster   Bucket   Multikv   Scrub   Join   Rare   ‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

Cluster   Associate   Stats   AVG   Transaction   Addtotals   Delta   Eval   Stddev   Rare   Outlier   Streamstats   Timechart   21

Splunk:  Big  Data  Security  Intelligence   Platform     Security  Intelligence     for  Business  

Machine  Data  

Security  Visualizations  for   Executives   Statistical  Analysis   s  

Proactive  Monitoring   Search  and  Investigation   22  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Enabling  IT  Risk  Scenarios  

Security  Relevant  Data   Confidentiality  /  Integrity  /  Availability  

CSO / CIO / CEO Views

App Mgmt

IT Ops

Compliance

Web Analytics

Applying IT Risk Scenarios µFinding Abnormal Behaviors¶

Business Analytics

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Open  Discussion   ‡ What  are  the  operational   challenges  with  security  big  data   analytics?   ‡ Political  issues?  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Questions?  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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Contact   Follow-­up:  [email protected]     Dave  Shackleford   [email protected]     Splunk     Mark  Seward   [email protected]  

‹7KH6$16Œ,QVWLWXWH-­  www.sans.org    

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