Beijing University of Posts &

Telecommunications

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Immune-inspired Online Method

for Service Interactions Detection

Jianyin Zhang,

Fangchun Yang,

Sen Su

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Agenda

• Introduction

• Analysis

• Our work

• Future work

• References

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Introduction

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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

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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

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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

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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]

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Analysis

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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

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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

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OUR WORK

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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]

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Our Work – 2

• Immune principles -- Negative selection

-- Antigen recognition

-- Co-stimulation

-- Immune memory

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Our Work – 3

• Service Interaction Detection System

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Our Work – 4

• Service Interaction Detection Process

Message encoding

Start

Message input

Message matching

Auxiliary detection I

No

Yes

End

Service interactionresolution

Information storage

No

No

Yes

Yes

Auxiliary detection II

Known Service interaction ?

Normal messageinteraction ?

New service interaction ?

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Our Work – 5

• Mapping relationship

Immune system Service interaction detection system

Antigen presentation Message encoding

Antigen recognition/Negative selection

Message matching

Co-stimulation Auxiliary detection II

Immune memory Information storage

Antigen elimination Service interaction resolution

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Our Work – 6

• Message encoding

-- extract detection-related information

-- encode according to the known service interaction phenomena and service composition language [22, 23]

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Our Work – 7

• Message matching -- R-contiguous-bits matching rule

A(110011) and B(000010) match for r≤3

A(110011) and B(000010) match for r≤3

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Our Work – 8

• Experiments

--- Detection efficiency √ Message matching time

√ Negative selection time

--- Detection accuracy

√ False-positive error rate

√ False-negative error rate

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Our Work – 9

• Summary -- Uniform mode

-- Online detection

-- Anomaly detection

-- Learning ability

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Future work

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Future Work

• Testing our system against other solutions

• Experiments on the efficiency and accuracy of the proposed method

• Online resolution method

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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

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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 compositi

on. 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 Ser

vices. 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.

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References - 3

Application of immune principles[14] Harmer, P.K.; Williams, P.D.; Gunsch, G.H.; Lamont, G.B., An artificial im

mune 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)

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Supported by • the National Basic Research and Development Pro

gram (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)

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Thank you！

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Any questions

marcozjy@gmail.com