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Anton Chuvakin on Security Data Centralization
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Anton Chuvakin on Security Data Centralization

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My old preso on data centralization; I might not agree with everything I said back then ...

My old preso on data centralization; I might not agree with everything I said back then ...

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Anton Chuvakin on Security Data Centralization Anton Chuvakin on Security Data Centralization Presentation Transcript

  • Centralizing Centralization v. 0.2 October 2003 Anton Chuvakin, Ph.D., GCIA, GCIH Senior Security Analyst
  • Outline
    • Security data centralization overview
    • Value of centralization
    • Single device type and cross-device centralization
    • Normalization, pros and cons
    • Categorization, security event types and standards
    • Correlation, types and methods
    • Why do you have to do it?
  • Terms
    • Message – some system indication that the event has occurred
    • Log or audit record – recorded message related to the event
    • Log file – collection of the above records
    • Alert – a message usually sent to notify an operator
    • Device – a source of security-relevant logs
  • Centralization
    • Centralized security controls:
    • Cheaper to manage
    • Easier to audit
    • Save money on staff
    • Reduce training costs
  • Security Data Overview
    • What data?
    • Audit logs
    • Transaction logs
    • Intrusion logs
    • Connection logs
    • System performance records
    • User activity logs
    • Various alerts
    • From where?
    • Firewalls/intrusion prevention
    • Routers/switches
    • Intrusion detection
    • Hosts
    • Business applications (databases, servers)
    • Anti-virus
    • VPNs
  • Centralized Data
    • Why centralize security data?
    • Accessibility
      • All audit records in one place
    • Cross-device searchability and analysis
      • Categorization
      • Correlation
    • De-duplication / volume reduction
    • Reduced response time
    • Increase in the efficiency of existing security point solutions
  • Requirements
    • What do you need to start?
    • Collect the data
    • Convert to common format
    • Reduce in size, if possible
    • Transport securely to a central location
    • Process in real-time
    • Alert on threats
    • Store securely
    • Report on trends
  • Challenges
    • Need to overcome these:
    • Too much data
    • Not enough data
    • Diverse records
    • False alarms
    • Duplicate data
    • Hard to get data
    • Chain of custody concerns
  • Case Made! So...
    • ...everybody build a “central console” for their stuff. Results : central consoles for ...
    • Each firewall vendor
    • Multiple firewalls and routers
    • Each IDS type
    • IDS and vulnerability scanners
    • Routers and network management data
    • Ad infinitum... 
  • Case I: NIDS Console
    • Relatively small number of devices
    • False alarms
    • False positives
    • Large volume (needs tuning)
    • Many of the recorded events require response
    • The data sometimes need to be viewed (and responded to) in near real-time
  • Example: ACID Console
    • Collects events from multiple Snort NIDS sensors
    • Uses relational DBMS to store data
    • Retains (and can show) full packet payload
    • Web front-end
    • Advanced search queries
      • Search across sensors
      • Search by any packet field/combination
    • Data graphing
    • No real-time tools
  • Case II: Desktop Protection
    • Such as personal firewall/IDS, anti-virus, host IPS
    • Characteristics:
    • Huge number of devices
    • Low volume from each device
    • Needs status monitoring (disabled by the user?)
    • Data might be transmitted over the slow WAN link
    • Rarely looked at
    • Requires cross-device correlation for meaningful analysis
  • Diverse devices
    • Moving from a single type of data source to heterogeneous sources
    • Volume is getting even higher
    • Data diversity problem arises
      • Binary and text logs
      • Undocumented formats
      • Free form logs
      • Same events described differently
      • Different level of detail in collected data
    • How to analyze?
  • Normalization Defined
    • Solution: normalization i.e. converting recorded events to a common format or schema (often XML)
    • How to normalize?
    • Look at common fields in security event records
      • Source IP, port, protocol
      • Event type
      • Device instance
      • Severity
    • Create a data model to cover all these and more
    • Map the original event fields to the new general schema
  • Normalization Example
  • Normalization
    • Advantages:
    • Store – known storage requirements
    • Analyze (c orrelate, c ategorize) and search – same attributes
    • Prioritize – uniform severity
    • Present/visualize – common reports
    • Challenges:
    • Data loss
      • What if something does not fit the model?
    • Overhead
      • Too much of a good thing?
    • Over-normalization
      • What if its not really the same ?
    • Mapping incompatibilities
      • Is this more of a source or a destination after all?
  • Categorization
    • Data format is the same, but what about the content ?
    • MSBlaster, Nimda, CodeRed -> Malware
    • Statd Attack, SSH Exploit -> Unix Exploits
    • UDP Bomb, Boink, Smurf -> Legacy DoS
    • Select Categories
    • Malware
    • Attacks and Exploits
    • Vulnerable Software
    • System Failures
    • AAA
    • Change Management
  • Value of Categorization
    • Value of Categorization
    • Adds intelligence to event data collection
    • Enhances high-level reporting
    • Provides understanding of the detected threat types and supplies the context for their interpretation
    • Challenges with Categorization
    • No universal standard
    • (but work in progress!)
    • Too much variety in data makes every categorization effort incomplete
    • Every security vendor is trying to create its own scheme (yak!)
  • Correlation
    • Defined :
    • General: “establishing or finding relationships between entities”
    • Security: “improving threat identification and assessment by looking not only at individual events, but at their sets , bound by some common parameter ('related')”
    • Correlation is enabled by centralization, normalization and also enhanced by categorization.
  • Correlation Types
    • Rule-based
      • Uses pre-existing knowledge of the attack (the rule) and is able to define what has been detected in precise terms
    • Statistical
      • Relies upon the knowledge of normal activities, which has been accumulated over time to detect the deviations
  • Policy, Vulnerability and Incident Management
    • Other data (information, knowledge)
    • Knowledge
      • Security policies and procedures
      • Industry and organization security guidelines
    • Vulnerability and asset data
      • Scans
      • Asset attributes
    • Security Incidents
      • Centralized incident handling and reporting
  • Conclusion
    • Centralization of security data is crucial for...
    • Large organization
      • Can't do security without it!
    • Small/medium companies
      • Needed to succeed with little security staff
  • Thanks for Viewing the Presentation
    • Anton Chuvakin, Ph.D., GCIA, GCIH, GCFA
    • http://www.chuvakin.org
    • Author of “Security Warrior” (O’Reilly) – http://www.securitywarrior.org
    • Read my blog at http:// chuvakin.blogspot.com
    • Book on logs is coming soon!
    • See http://www.info-secure.org for my papers, books, reviews and other security resources related to logs