Anomaly detection in data streams using fuzzy logic

2009 International Conference on Information and Communication Technologies, ICICT 2009

Volumn , Issue , 2009, Pages 167-174

  Khan, Muhammad Umair ^a  

^a Muhammad Ali Jinnah University   (Pakistan)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; CLUSTERING RESULTS; DATA STREAM; DATA STREAM CLUSTERING; DENSITY FUNCTIONS; HUMAN INTERVENTION; REAL-TIME DECISION MAKING; UNSUPERVISED DATA;

CLUSTERING ALGORITHMS; DATA COMMUNICATION SYSTEMS; DATA MINING; FUZZY LOGIC; FUZZY SETS;

CLUSTER ANALYSIS;

EID: 70449746530     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICICT.2009.5267196     Document Type: Conference Paper

References (29)

1. Horovitz O., Krishnaswamy S., and Gaber M. M., "A Fuzzy Approach for Interpretation and Application of Ubiquitous Data Stream Clustering", Proceedings of Second International Workshop on Knowledge Discovery in Data Streams, 16th European Conference on Machine Learning (ECML 2005) and the 9th European Conference on the Principals and Practice of Knowledge Discovery in Databases (PKDD 2005), Porto, Portugal, October 3-7, 2005.
2. www.wikipedia.org
3. Gurmeet Singh Manku, Rajeev Motwani. "Approximate Frequency Counts over Data Streams", In Proceedings of the 28th International Conference on Very Large Data Bases, Hong Kong, China, August 2002.
4. V. Barnett and T. Lewis, "Outliers in Statistical Data", 3rd Edition ISBN: 0471930946 Publication Date: Publisher: Wiley: 1994.
5. S. Bay and M. Schwabacher, "Mining distancebased outliers in near linear time with randomization and a simple pruning rule". In ACM SIGKDD, 2003.
6. F Cao, M Ester, W Qian, and A Zhou, "Densitybased Clustering over an Evolving Data Stream with Noise", Proceedings of the 2006 SIAM Conference on Data Mining, 2006.
7. M. Breunig et al, "LOF: Identifying density-based local outliers", In ACM SIGMOD, 2000.
8. E. Knorr et al, "Distance-based outliers: Algorithms and applications", VLDB Journal, 2000.
9. K. Sequeira and M. Zaki. ADMIT, "Anomalybased data mining for intrusions", In ACM SIGKDD, 2002.
10. A. Lazarevic et al, "A comparative study of outlier detection schemes for network intrusion detection", In SIAM Data Mining, 2003.
11. L. O'Callaghan, N. Mishra, A. Meyerson, S. Guha, and R. Motwani, "Streaming-data algorithms for high-quality clustering", Proceedings of IEEE International Conference on Data Engineering, March 2002.
12. Gaber, M. M., Krishnaswamy, S., Zaslavsky, A., "Cost-Efficient Mining Techniques for Data Streams", Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), Dunedin, New Zealand, 2004.
13. German Florez, Susan M. Bridges, and Rayford B. Vaughn, "An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection".
14. H. Güneş Kayacik, A. Nur Zincir-Heywood, Malcolm I, "Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets", Heywood Dalhousie University, Faculty of Computer Science, 6050 University Avenue, Halifax, Nova Scotia. B3H 1W5
15. Eric Bloedorn, Alan D. Christiansen, William Hill, Clement Skorupka, Lisa M. Talbot, Jonathan Tivel, "Data Mining for Network Intrusion Detection: How to Get Started"
16. C. Aggarwal, J. Han, J. Wang, P. S. Yu, "A Framework for Clustering Evolving Data Streams", Proc. 2003 Int. Conf. on Very Large Data Bases (VLDB'03), Berlin, Germany, Sept. 2003.
17. Dickerson, J. E, Dickerson, J. A, "Fuzzy network profiling for intrusion detection", Fuzzy Information Processing Society, NAFIPS, 19th International Conference of the North American. 2000.
18. Gurmeet Singh Manku, Rajeev Motwani. "Approximate Frequency Counts over Data Streams", In Proceedings of the 28th International Conference on Very Large Data Bases, Hong Kong, China, August 2002.
19. M. Charikar, K. Chen, And M. Farach-Colton, "Finding frequent items in data streams". In Proc. 29th Intl. Colloq. on Automata, Languages and Programming, 2002.
20. Tian Zhang, Raghu Ramakrishnan, Miron Livny, "BIRCH: an efficient data clustering method for very large databases", ACM SIGMOD Record archive Volume 25, Issue 2 June 1996, Pages: 103-114, ISSN: 0163-5808, New York, NY, USA.
21. Illick B., Gaber M. M., Krishnaswamy S., and Zaslavsky A., "Visualization of Cluster Dynamics and Change Detection in Ubiquitous Data Stream Mining", Third International Workshop on Knowledge Discovery in Data Streams June 29, 2006, Pittsburgh PA, USA.
22. Bo Sun, Fei Yu, Kui Wu, Victor C. M. Leung, "Mobility-based anomaly detection in cellular mobile networks", Proceedings of the 2004 ACM workshop on Wireless security Philadelphia, PA, USA, Pages: 61-69, Year of Publication: 2004, ISBN: 1-58113-925-X.
23. Horovitz, O., Krishnaswamy, S., and Gaber, M, M., "A Fuzzy Approach for Interpretation of Ubi qui t ous Data Stream Clustering and Its Application in Road Safety", Accepted for publication in Intelligent Data Analysis - Special Issue on Knowledge Discovery from Data Streams, Vol. 25, No. 1, 2006, IOS Press.
24. Gaber, M, M, Zaslavsky, A, and Krishnaswamy, S, "A Cost-Efficient Model for Ubiquitous Data Stream Mining", Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), Perugia Italy, July 4-9.
25. J. T. Yao, S. L. Zhao, L. V. Saxton, "A study on fuzzy intrusion detection", Proceedings of Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, 2005.
26. http://www.networkworld.com/columnists/2006/0 3130 6-faceoff-yes.html
27. http://ieeexplore.ieee.org
28. http://portal.acm.org
29. http://www.csse.monash.edu.au/∼mgaber/main. ht ml