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Data Mining for Security Applications
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Natural Disasters Human Errors Non - Information related threats Information Related threats Biological, Chemical, Nuclear Threats Critical Infrastructure Threats Threat Types
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Analysis of Firewall Policy Rules Using Data Mining Technique s ,[object Object],[object Object],[object Object],[object Object],[object Object]
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[object Object],[object Object],Firewall Policy Rule Firewall Log File Mining Log File Using Frequency Filtering Rule Generalization Generic Rules Identify Decaying & Dominant Rules Edit Firewall Rules
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Training data Feature extraction Clean or Infected ? Outgoing Emails Classifier Machine Learning Test data The Model ,[object Object],[object Object]
 
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Integrate data sources Clean/ modify data sources Build Profiles of Terrorists and Activities Examine results/ Prune results Report final results Data sources with information about terrorists and terrorist activities Mine the data
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Integrate data sources in real - time Build real - time models Examine Results in Real - time Report final results Data sources with information about terrorists and terrorist activities Mine the data Rapidly sift through data and discard irrelevant data
 
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data mining for security application

  • 1. Data Mining for Security Applications
  • 2.
  • 3.
  • 4. Natural Disasters Human Errors Non - Information related threats Information Related threats Biological, Chemical, Nuclear Threats Critical Infrastructure Threats Threat Types
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.  
  • 17.
  • 18. Integrate data sources Clean/ modify data sources Build Profiles of Terrorists and Activities Examine results/ Prune results Report final results Data sources with information about terrorists and terrorist activities Mine the data
  • 19.
  • 20. Integrate data sources in real - time Build real - time models Examine Results in Real - time Report final results Data sources with information about terrorists and terrorist activities Mine the data Rapidly sift through data and discard irrelevant data
  • 21.  
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.