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Reseach Article

Mining DoS attack sequences on Network Traffic using Fuzzy Time Interval

by Alpa Reshamwala, Sunita Mahajan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 25
Year of Publication: 2013
Authors: Alpa Reshamwala, Sunita Mahajan
10.5120/12220-7746

Alpa Reshamwala, Sunita Mahajan . Mining DoS attack sequences on Network Traffic using Fuzzy Time Interval. International Journal of Computer Applications. 70, 25 ( May 2013), 1-8. DOI=10.5120/12220-7746

@article{ 10.5120/12220-7746,
author = { Alpa Reshamwala, Sunita Mahajan },
title = { Mining DoS attack sequences on Network Traffic using Fuzzy Time Interval },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 25 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number25/12220-7746/ },
doi = { 10.5120/12220-7746 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:46.615053+05:30
%A Alpa Reshamwala
%A Sunita Mahajan
%T Mining DoS attack sequences on Network Traffic using Fuzzy Time Interval
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 25
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion of network which couldn't be analyzed, detected and prevented may make whole network system paralyze while the abnormal detection can prevent it by detecting the known and unknown character of data. Many intrusions aren't composed by single events, but by a series of attack steps in chronological order. Analyzing the order in which events occur can improve the attack detection accuracy and reduce false alarms. Intrusion is a multi step process in which a number of events must occur sequentially in order to launch a successful attack. Although conventional sequential patterns can reveal the order of attack events, the time between events can also be determined but it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, one either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets so that Dos attack sequential patterns are discovered on network traffic in fuzzy time interval. In this paper, an apriori based candidate generation algorithm has been implemented with Fuzzy time intervals to detect Dos attack sequences. The experimental results are also compared with the dataset which is generated by the SPMF sequential dataset generator.

References
  1. Guangjun Song, Zhenlong Sun, Xiaoye Li, "The Research of Association Rules Mining and Application in Intrusion Alerts Analysis", Second International Conference on Innovative Computing, Infonnatio and Control (ICICIC 2007),pp. 567, 2007.
  2. Zhan Jiuhua, "Intrusion Detection System Based on Data Mining", First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), pp. 402-405, 2008.
  3. Ya-Li Ding, Lei Li, Hong-Qi Luo, "A NOVEL SIGNATURe SEARCHING FOR INTRUSION DETECTION SYSTEM USING DATA MINING", Machine Learning and Cybernetics, 2009 International Conference on Volume I, pp. l22 - 126, 2009.
  4. Y. L. Chen, M. C. Chiang, and M. T. Ko, "Discovering time-interval sequential patterns in sequence databases", Expert Systems with Applications, Volume 25, Issue 3, October 2003, pp 343–354.
  5. Yen-Liang, Tony Cheng-Kui Huang, "Discovering Fuzzy Time-Interval Sequential Patterns in Sequence Databases", IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2005, vol. 35, pp. 959-972.
  6. Sunita Mahajan and Alpa Reshamwala, "Amalgamation of IDS Classification with Fuzzy techniques for Sequential pattern mining ",IJCA Proceedings on International Conference on Technology Systems and Management - ICTSM 2011, Number 3 - Article 7, pp 9–14.
  7. Sunita Mahajan and Alpa Reshamwala, "An Approach to Optimize Fuzzy Time-Interval Sequential Patterns Using Multi-objective Genetic Algorithm", ICTSM 2011, CCIS 145, pp. 115–120, 2011, Springer-Verlag Berlin Heidelberg 2011.
  8. R. Agrawal and R. Srikant, "Mining sequential patterns", In Proc. Int. Conf. Data Engineering, 1995, pp. 3–14.
  9. Pei, J. , Han, J. , Pinto, H. , Chen, Q. , Dayal, U. , & Hsu, M. -C. , "PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth", Proceedings of 2001 International Conference on Data Engineering, pp. 215–224.
  10. Han, J. , Pei, J. , Mortazavi-Asl, B. , Chen, Q. , Dayal, U. , & Hsu, M. -C. , "FreeSpan: Frequent pattern-projected sequential pattern mining", Proceedings of 2000 International Conference on Knowledge Discovery and Data Mining, pp. 355–359.
  11. Srikant, R. , & Agrawal, R. , "Mining sequential patterns: Generalizations and performance improvements", Proceedings of the 5th International Conference on Extending Database Technology,1996, pp. 3–17.
  12. Zaki, M. J. , "SPADE: An efficient algorithm for mining frequent sequences", volume 42 Issue 1-2, January-February 2001, pp 31–60.
  13. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules", Proceedings of 20th VLDB Conference Santiago, Chile, 1994, pp. 487–499.
  14. Yangdong Ye, Qing Zhou , Xu Wang, Limin Jia, "Analysis of fuzzy time interval in the hybrid Petri net model of train operation system", 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 2010. Vol. 1 pp. V1-644 - V1-648.
  15. Chung-I Chang, Hao-En Chueh , Lin, N. P. , "Sequential Patterns Mining with Fuzzy Time-Intervals", Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09, Vol 3,pp. 165 – 169.
  16. Chung-I Chang, Hao-En Chueh, Yu-Chun Luo, "An integrated sequential patterns mining with fuzzy time-intervals", International Conference on Systems and Informatics (ICSAI), 2012, pp. 2294 - 2298.
  17. S. Hofmeyr, S. Forrest, A. Somayaji. "Intrusion Detection Using Sequences of System Calls," Journal of Computer Security, 1998. Vol. 6 pp. 151-180.
  18. Yin, Qing-Bo, Zhang, Ru-Bo, Li, Xue-Yao and Wang, Hui-Qiang "Research on Technology of Intrusion Detection Based on Linear Prediction and Markov M odel," Chinese Journal of Computers, 2005, Vol. 28, no. 5, pp. 900-907.
  19. Lee W, Stolfo L S, Mok K W. "A Data Mining Framework for Adaptive Intrusion Detection," Proceedings of the 1999 IEEE Symposium on Security and Privacy, 1999. pp. 120-132.
  20. Karlton Sequeira, Mohammed Zkai. "ADMIT: anomaly-based data mining for intrusions," Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002.
  21. XUE Anrong, HONG Shijie, JU Shiguang, CHEN Weihe, "Application of Sequential Patterns Based on User's Interest in Intrusion Detection", Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education, pp 1089- 1093, 2008.
  22. Zhang Shengbin, XI Hongsheng, WANG Weiping. "Computer Intrusion Detection Based on PrefixSpan," Computer Engineering, 2003.
  23. SONGShi-Jie, HUANGZun-Guo, HUHua- Ping, JINShi-Yao. "A Sequential Pattem Mining Algorithm for Misuse Intrusion Deteetion," Workshop: Information Security and Survivability for Grid. Oct. 21-24, 2004, Wuhan, China. pp. 458-465.
  24. CAI Weihong, LIU Zhen and WANG Meilin, "Intrusion Detection Based on Fuzzy Logic and Immune GA," Computer Engineering, 2006.
  25. Duan Yi-feng , Hu Gu-yu , Ding Li. "An Application of Sequential Pattern Mining in Network Alarm Data Analyses," Journal of Beijing University of Posts and Telecommunications, 2004. 12.
  26. XIN Hong-liang, OUYANG Wei-min and ZHU Wan-tao,. "Audit-oriented sequence mining algorithm with strict constraints," Computer Applications, 2006.
  27. Weijun Zhu, Qinglei Zhou, Ping Li, "Intrusion detection based on model checking timed interval temporal logic", IEEE International Conference on Information Theory and Information Security (ICITIS), 2010, pp. 503 – 505.
  28. Milanesi, G. , Sarti, A. , Tubaro, S. , " Robust real-time intrusion detection with fuzzy classification", International Conference on Image Processing. 2002, vol. 3,pp. III-437 - III-440.
  29. Kai Xing Wu, Juan Hao, Chunhua Wang, "Application of Fuzzy Association Rules in Intrusion Detection", International Conference on Internet Computing & Information Services (ICICIS), 2011, pp. 269 – 272.
  30. Yongzhong Li, Rushan Wang , Jing Xu, Ge Yang, Bo Zhao, "Intrusion Detection Method Based on Fuzzy Hidden Markov Model", Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09, vol. 3, pp. 470 – 474.
  31. Jianxiong Luo, Bridges, S. M. , Vaughn, R. B. , Jr. , "Fuzzy frequent episodes for real-time intrusion detection", The 10th IEEE International Conference on Fuzzy Systems, 2001, vol. 1, pp. 368 – 371.
  32. Ming-Yang Su, Sheng-Cheng Yeh, Kai-Chi Chang, Hua-Fu Wei, "Using Incremental Mining to Generate Fuzzy Rules for Real-Time Network Intrusion Detection Systems", 22nd International Conference on Advanced Information Networking and Applications - Workshops, 2008. AINAW 2008, pp. 50 – 55.
  33. Xiaogang Wang, Junzhou Luo, Ming Yang, "An efficient sequential watermark detection model for tracing network attack flows", IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2012, p. 236 – 243.
  34. Reshamwala, A. , Mahajan, S. , "Prediction of DoS attack sequences", International Conference on Communication, Information & Computing Technology (ICCICT), 2012, pp. 1 – 5.
  35. Reshamwala, A. , Mahajan, S. , "Detection of DoS attack time interval sequences on network traffic", World Congress on Information and Communication Technologies (WICT), 2012, pp. 739 – 744.
  36. Lee W and Stolfo S J, "Data mining approaches for intrusion detection", Proceedings of the 7th USENIX Security Symposium, :26-29, 1998.
  37. SPMF: "Sequential Pattern Mining Framework".
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Fuzzy Set Sequential pattern Time interval Intrusion detection system DoS attacks Apriori