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Immune-inspired Online Method for Service Interactions Detection

Immune-inspired Online Method for Service Interactions Detection. Jianyin Zhang, Fangchun Yang, Sen Su. Agenda. Introduction Analysis Our work Future work References. Introduction. Introduction – 1.1. Feature interaction problem

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Immune-inspired Online Method for Service Interactions Detection

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  1. Immune-inspired Online Method for Service Interactions Detection Jianyin Zhang, Fangchun Yang, Sen Su

  2. Agenda • Introduction • Analysis • Our work • Future work • References

  3. Introduction

  4. Introduction – 1.1 • Feature interaction problem -- firstly coined in the telecommunication area by Bellcore • Definition -- interactions that occur because the requirements of multiple features are not compatible, AND interactions that occur when a feature behaves differently in the presence of other features -- Example: CFU vs CW

  5. Introduction – 1.2 • Current work -- summarized in [1 ~ 5] -- FIW’92 → ICFI’07 -- focused on the telecommunication and software system -- major research trends: software engineering approaches, formal methods, and on-line techniques

  6. Introduction – 2.1 • Service interactions problem -- FIW’00 FIW’03 (M. Weiss) • Background -- Limitation of individual Web service -- Introduction of service composition in the Web Services area -- Complex message interactions among composed services

  7. Introduction – 2.2 • Classification -- Functional Race condition, Resource contention, etc. -- Non-functional Privacy, Security, Usability, Performance, etc. • Current work -- mostly on the service interaction detection -- “divide-and-rule” approach -- static methods URN[6, 7], CRESS[9], PetriNet[10], LTS[11]

  8. Analysis

  9. Analysis– 1 • Drawbacks of current methods --- Limitation of application fields --- Hard to be integrated --- Not effective for unknown service interaction detection --- Deficiencies of formal methods √ Destroy the privacy of service logic √ Strong mathematical skills √State explosion problem √ Applied before the runtime

  10. Analysis– 2 • What we think A robust detection system should -- Online detection -- Uniform manner -- Effective for the unknown interactions -- Not destroy the privacy of atomic service logic

  11. OUR WORK

  12. Our Work – 1 • Motivation of immune-inspired method -- functional similarity between immune system and WSFI detection system -- online self-protection system -- same problem of how to improve the detection efficiency and how to reduce the false rate -- application of immune principles in the dynamic detection system [12 ~ 18]

  13. Our Work – 2 • Immune principles -- Negative selection -- Antigen recognition -- Co-stimulation -- Immune memory

  14. Our Work – 3 • Service Interaction Detection System

  15. Start No Normal message interaction ? Auxiliary detection I Message input Yes Yes Known Service interaction ? Service interaction resolution No Message encoding Auxiliary detection II Message matching Information storage Yes New service interaction ? No End Our Work – 4 • Service Interaction Detection Process

  16. Our Work – 5 • Mapping relationship

  17. Our Work – 6 • Message encoding -- extract detection-related information -- encode according to the known service interaction phenomena and service composition language [22, 23]

  18. Our Work – 7 A(110011) and B(000010) match for r≤3 • Message matching -- R-contiguous-bits matching rule A(110011) and B(000010) match for r≤3

  19. Our Work – 8 • Experiments --- Detection efficiency √ Message matching time √ Negative selection time --- Detection accuracy √ False-positive error rate √ False-negative error rate

  20. Our Work – 9 • Summary -- Uniform mode -- Online detection -- Anomaly detection -- Learning ability

  21. Future work

  22. Future Work • Testing our system against other solutions • Experiments on the efficiency and accuracy of the proposed method • Online resolution method

  23. References - 1 Feature interaction [1] Lynne Blair, Gordon Blair, Jianxiong Pang, Christos Efstratiou, “Feature Interaction outside a Telecom domain”, FICS 2001. Proceedings, June 18-22, 2001, Pages:15 – 20 [2] Calder M, Kolberg M, Magill E, et al. “Feature interaction: a critical review and considered forecast”, International Journal of Telecommunication and Computer Networks, 2003, 41 (1): pp 115-141. [3] Keck D. O. and Kuehn P.J. “The Feature and Service Interaction Problem in Telecommunications Systems: A Survey”. IEEE Transactions on Software Engineering, October 1998. 9, 24(10):pp 779--796 [4] EJ Cameron et al. , “A Feature Interaction Benchmark for IN and Beyond”, in Feature Interactions in Telecommunications Systems, IOS press, 1994, pp. 1-23 [5] Amyot D. and Logrippo L, “Guest editorial: Directions in feature interaction research”, Computer Networks, Special issue on Feature Interactions in Emerging Application Domains, Vol. 45, No. 5, 5 August 2004, pp563-567 [6] Weiss, M., and Esfandiari, B., “On Feature Interactions among Web Services”, International Journal of Web Services Research, 2(4), 21-45, October- December, 2005

  24. References - 2 [7] Michael Weiss, Babak Esfandiari, and Yun Luo, Towards a Classification of Web Service Feature Interactions, Third International Conference on Service Oriented Computing (ICSOC05), Amsterdam, Netherlands, 2005 [8] Kenneth J. Turner. Formalising Web Services. Formal Techniques for Networked and Distributed Systems (FORTE XVIII), LNCS 3731, October 2005: 473-488 [9] Jianyin Zhang, Sen Su, Fangchun Yang, Detecting Race Conditions in Web Services, In: Proceedings of the International Conference on Internet and Web Applications and Services (ICIW'06), French, February 2006 Web service composition [10] Schahram Dustdar, Wolfgang Schreiner, A survey on web services composition. International Journal of Web and Grid Services, 2005, 1(1):1-30 [11] Milanovic N, Malek M. Current solutions for Web service composition. IEEE Internet Computing, 2004,18(6):51-59 [12] T. Andrews et al., editors. Business Process Execution Language for Web Services. Version 1.1. BEA, IBM, Microsoft, SAP, Siebel, May 2003. [13] A. Arkin et al., editors. Web Services Business Process Execution Language. Version 2.0.OASIS, Billerica, Massachusetts, Feb. 2005.

  25. References - 3 Application of immune principles [14] Harmer, P.K.; Williams, P.D.; Gunsch, G.H.; Lamont, G.B., An artificial immune system architecture for computer security applications, IEEE Transactions on Evolutionary Computation, Volume 6, Issue 3, June 2002: 252 – 280 [15] S. Hofmeyr and S. Forrest, Architecture for an Artificial Immune System, Evolutionary Computation Journal, 7(1), 2000, Page(s): 45 – 68 [16] Dasgupta, D., Gonzalez, F., An immunity-based technique to characterize intrusions in computer networks, IEEE Transactions on Evolutionary Computation, June 2002, 6(3): 281 – 291 [17] Branco, P.J.C., Dente, J.A., Mendes, R.V., Using immunology principles for fault detection, IEEE Transactions on Industrial Electronics, April 2003, 50(2):362 -373 [18] Xiong Wenjian, An online NGN service interaction detection method based on immunology theory (Ph.D. dissertation), Beijing: School of Computer Science and Technology, Beijing University of Posts and Telecommunications, 2005 (in Chinese)

  26. Supported by • the National Basic Research and Development Program (973 program) of China under Grant No.2003CB314806; • the Program for New Century Excellent Talents in University (No: NCET-05-0114); • the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT)

  27. Thank you！

  28. Any questions • marcozjy@gmail.com

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