ANALYSIS, DESIGN AND IMPLEMENTATION OF IDS USING DATA MINING

B. V. Patel, B. Meshram

Abstract

To achieve the implementation of intrusion detection system (IDS), we have integrated the Fuzzy Logic with extended Apriori Association Data Mining to extract more abstract patterns at a higher level which look for deviations from stored patterns of normal behaviour of the computer network. Here the various packet formats of TCP, UDP, IP etc are used to study the normal behaviour of the network. Genetic algorithms are used to tune the fuzzy membership functions. The tuned data by genetic algorithms is processed by the modified Apriori algorithm. The association pattern is populated by genetic algorithm for the selection of best population of the network traffic. This best populated data is classified by the C4.5 algorithms to find intrusions. The deployment of IDS is done under the control of secure linux environment and the system is tested in the distributed environment.

References

  1. B. B. Meshram, Alok K. Kumar, 2004. HyIDS: Hybrid Intrusion Detection System in the Proceedings of National Conference on Research & Practices in Current Areas of IT, Department of Computer Science & Engineering, Sant Harchand Sing Longowal Central Institute of Engineering & Technology, Longowal, Dist Sangar( Punjab)-148106.
  2. Alok K. Kumar , B. B. Meshram, 2005. NAD: Statistical Network Anomaly Detector. In International Conference Systemics, Cybernetics and Informatics Icsci - 2005, Under The Aegis Of Pentagram Research Centre Pvt. Ltd. Venue: Dr. Mcr Hrd Institute Of Andhra Pradesh, Hyderabad.
  3. B. B. Meshram, T.R.Sontakke, 2001. Object Oriented Database schema Design. In 7Th International Conference on Object Oriented Information Systems., Calgary, Canada.
  4. Z. Malik, B. B. Meshram, 2004. A Study on Data Mining. In Proceedings of National Conference on Research & Practices in Current Areas of IT, Department of Computer Science & Engineering, Sant Harchand Sing Longowal Central Institute of Engineering & Technology, Longowal, Dist Sangar( Punjab)-148106.
  5. S. S. Karvande, B.B.Meshram, 2004. Design And Implementation Of Application Layer Firewall For Secure Internet Access. In International Conferences on Soft Computing, Department of Computer Applications, Computer Science & Engineering, Information Technology, Bharath Institute of Higher Education & Research, Chennai, Tamilnadu.
  6. B. V. Patel, B. B. Meshram, 2007. Carpace 1.0 For Multimedia Email Security. In International Multiconference of Engineers and Computer Scientists, Hong Kong.
  7. Fan W., Miller M., Stolfo S., Lee W., Chan P, 2001. Using Artificial Anomalies to Detect Unknown and Known Network Intrusions. In Proceedings of the First IEEE International Conference on Data MiningCA, http://www.cc.gatech.edu/wenke/papers/artificial_an omalies.ps.
  8. Axelsson S. 2000. Intrusion Detection Systems: A Taxomomy and Survey. Technical report No 99-15, Dept. of Computer Engineering, Chalmers University of Technology, Sweden. http://www.ce.chalmers.se/ staff/sax/taxonomy.ps.
  9. Frederick K. K. 2001, Network Intrusion Detection Signatures. http://online.securityfocus.com/infocus/ 1524.
  10. Marin J., Ragsdale D., Surdu J, 2001. A Hybrid Approach to the Profile Creation and Intrusion Detection. In Proceedings of the DARPA Information Survivability Conference and Exposition.
  11. Lee W.,2000.A data mining and CIDF based approach for detecting novel and distributed Intrusions. In Third International Worksho on Recent Advances in Intrusion Detection, RAID. Toulouse, France. http://www.cc.gatech.edu/wenke/papers/lee_raid_00.ps.
  12. Elson D., 2000. Intrusion Detection, Theory and Practice. http://online.securityfocus.com/infocus/1203.
  13. NSS Group, 2002. Intrusion Detection Systems http://www.nss.co.uk/ids/edition3/index.htm.
  14. Jones A. K., Sielken R. S., 2000. Computer system intrusion detection: a survey.
  15. http://www.cs.virginia.edu/jones/IDS-research/ Documents/jones-sielken-survey-v11.pdf.
  16. Carvalho, D.R., Freitas, 2002, Genetic Algorithm with sequential with sequential niching for discovering small disjunct rules. In proceedings of Genetic and Evolutionary Computation Conference.
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Paper Citation


in Harvard Style

V. Patel B. and Meshram B. (2008). ANALYSIS, DESIGN AND IMPLEMENTATION OF IDS USING DATA MINING . In Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8111-26-5, pages 81-86. DOI: 10.5220/0001516100810086


in Bibtex Style

@conference{webist08,
author={B. V. Patel and B. Meshram},
title={ANALYSIS, DESIGN AND IMPLEMENTATION OF IDS USING DATA MINING},
booktitle={Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2008},
pages={81-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001516100810086},
isbn={978-989-8111-26-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - ANALYSIS, DESIGN AND IMPLEMENTATION OF IDS USING DATA MINING
SN - 978-989-8111-26-5
AU - V. Patel B.
AU - Meshram B.
PY - 2008
SP - 81
EP - 86
DO - 10.5220/0001516100810086