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Intrusion Detection System with Data Mining Approach: A Review

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Intrusion Detection System with Data Mining Approach: A Review

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Abstract

Despite of growing information technology widely, security has remained one challenging area for computers and networks. Recently many researchers have focused on intrusion detection system based on data mining techniques as an efficient strategy. The main problem in intrusion detection system is accuracy to detect new attacks therefore unsupervised methods should be applied. On the other hand, intrusion in system must be recognized in realtime, although, intrusion detection system is also helpful in off-line status for removing weaknesses of network’s security. However, data mining techniques can lead us to discover hidden information from network’s log data. In this survey, we try to clarify: first,the different problem definitions with regard to network intrusion detection generally; second, the specific difficulties encountered in this field of research; third, the varying assumptions, heuristics, and intuitions forming the basis of erent approaches; and how several prominent solutions tackle different problems.

References

17 Cites in Article
  1. 1. Yasser Yasami,Saadat Mozaffari (2010). A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning methods. The Journal of Supercomputing, 53(1), 231-245.
  2. 2. B Thuraisingham,L Khan,M Masud,K Hamlen (2009). Data mining for security applications. Data mining for security applications
  3. 3. Su-Yun Wu,Ester Yen (2009). Data mining-based intrusion detectors. Expert Systems with Applications, 36(3), 5605-5612.
  4. 4. F Xie,S Bai (2006). Using Data Field to Analyze Network Intrusions. Information Security Practice and Experience, 78-89.
  5. 5. S Jiang,X Song,H Wang,J Han,Q Li (2006). A clustering-based method for unsupervised intrusion detections. Pattern Recognition Letters, 27, 802-810.
  6. 6. W Lee,S Stolfo,K Mok (2002). A data mining framework for building intrusion detection models. A data mining framework for building intrusion detection models
  7. 7. Sheng Sun,Yuanzhen Wang (2009). A Weighted Support Vector Clustering Algorithm and its Application in Network Intrusion Detection. 2009 First International Workshop on Education Technology and Computer Science, 352-355.
  8. 8. M Koufakou,Georgiopoulos (2010). A fast outlier detection strategy for distributed high-©2011 Global Journals Inc. (US) dimensional data sets with mixed attributes. Data Mining and Knowledge Discovery, 20, 259-289.
  9. 9. Hongwei Gao,Dingju Zhu,Xiaomin Wang (2010). A Parallel Clustering Ensemble Algorithm for Intrusion Detection System. 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 450-453.
  10. 10. S Alam,G Dobbie,P Riddle,M Naeem (2010). A swarm intelligence based clustering approach for outlier detection. A swarm intelligence based clustering approach for outlier detection
  11. 11. H Liang,R Wei-Wu,R Fei (2009). An Adaptive Anomaly Detection Based on Hierarchical Clustering. An Adaptive Anomaly Detection Based on Hierarchical Clustering
  12. 12. P Gogoi,B Borah,D Bhattacharyya (2010). Anomaly Detection Analysis of Intrusion Data using Supervised & Unsupervised Approach. Journal of Convergence Information Technology, 5
  13. 13. Goverdhan Singh,Florent Masseglia,Céline Fiot,Alice Marascu,Pascal Poncelet (2010). Mining Common Outliers for Intrusion Detection. Studies in Computational Intelligence, 217-234.
  14. 14. G Zhang,S Zhang,Sun (2009). A Mixed Unsupervised Clustering-Based Intrusion Detection Model. A Mixed Unsupervised Clustering-Based Intrusion Detection Model
  15. 15. Li-Ye Tian,Wei-Peng Liu (2010). Incremental intrusion detecting method based on SOM/RBF. 2010 International Conference on Machine Learning and Cybernetics, 2849-2853.
  16. 16. H Wang,Y Zhang,D Li (2010). Network intrusion detection based on hybrid Fuzzy Cmean clustering. Network intrusion detection based on hybrid Fuzzy Cmean clustering
  17. 17. S Teng,H Du,N Wu,W Zhang,J Su (2010). A Cooperative Network Intrusion detection Based on Fuzzy SVMs. Journal of Networks, 5, 475.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

DR. Madjid Khalilian, Norwati Mustapha. . "Intrusion Detection System with Data Mining Approach: A Review". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 5).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST Classification FOR Code: 080603
080609
080605
Version of record
v1.2

Issue date
April 14, 2011

Language
English
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Intrusion Detection System with Data Mining Approach: A Review

DR. Khalilian
DR. Khalilian university putra malaysia
Norwati Mustapha
Norwati Mustapha <p>university putra malaysia</p>