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Presented By:                               Guided By:
Aakash Khandelwal                           Prof Bansode.N.S
Dilip Jangam
Sachin Rupnawar




                    Sinhagad Institute Of Technology   3/8/2013   1
   Introduction
   Need of IDS
   Types of IDS
   Literature Survey
   Proposed System
   Framework
   Applications
   Advantages & Limitations
   Conclusion
   References

                           Sinhagad Institute Of Technology   3/8/2013   2
   Intruder
   Intrusion Detection System(IDS)[5]
   IDS Alerts[1]
   False alarm[1]
   Rule Classes[1]




                            Sinhagad Institute Of Technology   3/8/2013   3
   Denial of service[1]
   Threat to Confidentiality[1]
   Modification of contents[1]
   Masquerade[1]
   IDS and Firewalls




                             Sinhagad Institute Of Technology   3/8/2013   4
   Host Based IDS[1][3]

   Network Based IDS[1][6]




                           Sinhagad Institute Of Technology   3/8/2013   5
 Genetic Algorithms[2]
 NeuralNetwork[1]
 Data Mining Approaches[1]
 Drawbacks Of Existing System[1]




                          Sinhagad Institute Of Technology   3/8/2013   6
   Detection of ‘False Intrusion Alerts’ 3 Stages
          -Alert preprocessing phase
          -Model constructing Phase
          -Rule Defining Phase

   Removal of resource usage problem




                              Sinhagad Institute Of Technology   3/8/2013   7
Fig 1:Decision Support System Architecture[1]

                       Sinhagad Institute Of Technology   3/8/2013   8
Fig 2:Decision Support System in Experiment[1]


                         Sinhagad Institute Of Technology   3/8/2013   9
 Defence
 Banking
 Database
 Antivirus




              Sinhagad Institute Of Technology   3/8/2013   10
   Detect attack that cannot be seen by NIDS
   Can operate in an environment in which network
    traffic is encrypted
   Removes Resource Usage Problem




                            Sinhagad Institute Of Technology   3/8/2013   11
   Since at least the information sources reside on the host
    targeted by attacks, the IDS may be attacked and
    disabled as port of the attack
   Since they use the computing resources of the hosts they
    are monitoring, therefore inflicting a performance cost
    on the monitored systems




                               Sinhagad Institute Of Technology   3/8/2013   12
   The alert classication model is useful for experts to
    discover suspicious or intrusion patterns quickly and
    precisely, and lightens the load of on-line alert
    analysis for experts.




                             Sinhagad Institute Of Technology   3/8/2013   13
[1] Yan Zhang; Shuguang Huang; Yongyi Wang; , "IDS Alert Classification Model
   Construction Using Decision Support Techniques," Computer Science and
   Electronics Engineering (ICCSEE), 2012 International Conference on , vol.1, no.,
   pp.301-305, 23-25 March 2012
[2] Owais, S.; Snasel, V.; Kromer, P.; Abraham, A.; , "Survey: Using Genetic Algorithm
   Approach in Intrusion Detection Systems Techniques," Computer Information
   Systems and Industrial Management Applications, 2008. CISIM '08. 7th , vol., no.,
   pp.300-307, 26-28 June 2008
[3] Lin Ying; Zhang Yan; Ou Yang-jia; , "The Design and Implementation of Host-
   Based Intrusion Detection System," Intelligent Information Technology and Security
   Informatics (IITSI), 2010 Third International Symposium on , vol., no., pp.595-598,
   2-4 April 2010
[4] William Stallings, “Cryptography and Network Security”, Principles and Practices,
   Third
      Edition.
[5] D. E. Denning, "An intrusion-detection model". IEEE Transactions on Software
     Engineering, Vol. SE-13(No. 2):222-232, Feb. 1987.
[6] Stephen Northcutt, Judy Novak, “Network Intrusion Detection”, Third Edition,
   Pearson
     Education 2003.




                                          Sinhagad Institute Of Technology   3/8/2013    14
Thank You!!!!!




       Sinhagad Institute Of Technology   3/8/2013   15

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IDS alert classification model

  • 1. Presented By: Guided By: Aakash Khandelwal Prof Bansode.N.S Dilip Jangam Sachin Rupnawar Sinhagad Institute Of Technology 3/8/2013 1
  • 2. Introduction  Need of IDS  Types of IDS  Literature Survey  Proposed System  Framework  Applications  Advantages & Limitations  Conclusion  References Sinhagad Institute Of Technology 3/8/2013 2
  • 3. Intruder  Intrusion Detection System(IDS)[5]  IDS Alerts[1]  False alarm[1]  Rule Classes[1] Sinhagad Institute Of Technology 3/8/2013 3
  • 4. Denial of service[1]  Threat to Confidentiality[1]  Modification of contents[1]  Masquerade[1]  IDS and Firewalls Sinhagad Institute Of Technology 3/8/2013 4
  • 5. Host Based IDS[1][3]  Network Based IDS[1][6] Sinhagad Institute Of Technology 3/8/2013 5
  • 6.  Genetic Algorithms[2]  NeuralNetwork[1]  Data Mining Approaches[1]  Drawbacks Of Existing System[1] Sinhagad Institute Of Technology 3/8/2013 6
  • 7. Detection of ‘False Intrusion Alerts’ 3 Stages -Alert preprocessing phase -Model constructing Phase -Rule Defining Phase  Removal of resource usage problem Sinhagad Institute Of Technology 3/8/2013 7
  • 8. Fig 1:Decision Support System Architecture[1] Sinhagad Institute Of Technology 3/8/2013 8
  • 9. Fig 2:Decision Support System in Experiment[1] Sinhagad Institute Of Technology 3/8/2013 9
  • 10.  Defence  Banking  Database  Antivirus Sinhagad Institute Of Technology 3/8/2013 10
  • 11. Detect attack that cannot be seen by NIDS  Can operate in an environment in which network traffic is encrypted  Removes Resource Usage Problem Sinhagad Institute Of Technology 3/8/2013 11
  • 12. Since at least the information sources reside on the host targeted by attacks, the IDS may be attacked and disabled as port of the attack  Since they use the computing resources of the hosts they are monitoring, therefore inflicting a performance cost on the monitored systems Sinhagad Institute Of Technology 3/8/2013 12
  • 13. The alert classication model is useful for experts to discover suspicious or intrusion patterns quickly and precisely, and lightens the load of on-line alert analysis for experts. Sinhagad Institute Of Technology 3/8/2013 13
  • 14. [1] Yan Zhang; Shuguang Huang; Yongyi Wang; , "IDS Alert Classification Model Construction Using Decision Support Techniques," Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on , vol.1, no., pp.301-305, 23-25 March 2012 [2] Owais, S.; Snasel, V.; Kromer, P.; Abraham, A.; , "Survey: Using Genetic Algorithm Approach in Intrusion Detection Systems Techniques," Computer Information Systems and Industrial Management Applications, 2008. CISIM '08. 7th , vol., no., pp.300-307, 26-28 June 2008 [3] Lin Ying; Zhang Yan; Ou Yang-jia; , "The Design and Implementation of Host- Based Intrusion Detection System," Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on , vol., no., pp.595-598, 2-4 April 2010 [4] William Stallings, “Cryptography and Network Security”, Principles and Practices, Third Edition. [5] D. E. Denning, "An intrusion-detection model". IEEE Transactions on Software Engineering, Vol. SE-13(No. 2):222-232, Feb. 1987. [6] Stephen Northcutt, Judy Novak, “Network Intrusion Detection”, Third Edition, Pearson Education 2003. Sinhagad Institute Of Technology 3/8/2013 14
  • 15. Thank You!!!!! Sinhagad Institute Of Technology 3/8/2013 15

Editor's Notes

  1. have been used to discover either unknown or useful patterns for experts, lots of hidden and concealed intrusion patterns may still be escaped because of insufficient and dirty Our idea is to construct a decision support system to help experts construct an alert classification model for on-line intrusion detection of IDS alerts.information.