research-article

Forensics-aware web services composition and ranking

Authors:

Mohsen Rouached,

Mohamed AbidAuthors Info & Claims

iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services

Article No.: 56, Pages 1 - 10

https://doi.org/10.1145/2837185.2837226

Published: 11 December 2015 Publication History

Abstract

Web service composition has been extensively studied in recent years. Although a lot of new models and mechanisms have been proposed, many issues in service composition still remain unsolved. Among them, forensics examination is one of the major concerns. As opposed to traditional forensics implementations, applying forensics to Web service infrastructures introduces novel problems such as the need for neutrality, comprehensiveness, and reliability. Existing approaches fail to recognize that even optimized strategies for service selection and composition involve the exchange of large amounts of potentially sensitive data, causing potentially serious forensics leaks. Consequently, forensics is still among the key challenges that keep hampering service composition-based solutions.

In this context, this paper proposes a built in forensics-aware framework for Web services (Fi4SOA). Fi4SOA uses Sherwood Applied Business Security (SABSA) methodology to merge forensics properties with business requirements at service design phase. It uses reasoning machine over a new proposed ontology to define forensics properties and monitor forensics events at run time phase.

References

[1]

A. Akremi, H. Sallay, and M. Rouached. An efficient intrusion alerts miner for forensics readiness in high speed networks. IJISP, 8(1):62--78, 2014.

[2]

A. R. Amran, R. C.-W. Phan, and D. J. Parish. Metrics for network forensics conviction evidence. In ICITST, pages 1--8. IEEE, 2009.

[3]

G. Baryannis, O. Danylevych, D. Karastoyanova, K. Kritikos, P. Leitner, F. Rosenberg, and B. Wetzstein. Service composition. In Service Research Challenges and Solutions for the Future Internet - S-Cube - Towards Engineering, Managing and Adapting Service-Based Systems, pages 55--84, 2010.

[4]

T. Berners-Lee and D. Connolly. Notation3 (n3): A readable rdf syntax. W3c team submission, W3C, January 2008.

[5]

T. Berners-Lee, J. Hendler, and O. Lassila. The semantic web. Scientific American, 284(5):34--43, May 2001.

[6]

E. Casey. Digital Evidence and Computer Crime - Forensic Science, Computers and the Internet, 3rd Edition. Academic Press, 2011.

[7]

F. Cohen, J. Lowrie, and C. Preston. The state of the science of digital evidence examination. In G. L. Peterson and S. Shenoi, editors, IFIP Int. Conf. Digital Forensics, volume 361 of IFIP Advances in Information and Communication Technology, pages 3--21. Springer, 2011.

[8]

S. M. S. da Cruz, L. M. Campos, M. L. M. Campos, and P. F. Pires. A data mart approach for monitoring web services usage and evaluating quality of services. In A. H. F. Laender, editor, SBBD, pages 267--280. UFAM, 2003.

[9]

R. Danyliw, J. Meijer, and Y. Demchenko. The incident object description exchange format. In RFC 5070 (Proposed Standard), 2007.

[10]

L. Duranti and C. Rogers. Trust in digital records: An increasingly cloudy legal area. Computer Law & Security Review, 28(5):522--531, 2012.

[11]

J. Dykstra and A. T. Sherman. Design and implementation of frost: Digital forensic tools for the openstack cloud computing platform. Digit. Investig., 10:S87--S95, Aug. 2013.

Digital Library

[12]

S. A. T. Dykstra, Josiah. Acquiring forensic evidence from infrastructure-as-a-service cloud computing: Exploring and evaluating tools, trust, and techniques. Digital Investigation, 9(Supplement):S90--S98, 2012.

[13]

X. F. Xiao, S. Jin. A novel data mining-based method for alert reduction and analysis. Journal of Networks, pages 88--97, 2010.

[14]

M. Gunestas, M. Mehmet, D. Wijesekera, and A. Singhal. Forensic web services framework. IT Professional, 13(3):31--37, 2011.

Digital Library

[15]

G. A. Hall and W. P. Davis. Toward defining the intersection of forensics and information technology. IJDE, 4,(1), 2005, 2005.

[16]

J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov., 8(1):53--87, Jan. 2004.

Digital Library

[17]

A. Herzberg and I. Yoffe. The delivery and evidences layer, 2007.

[18]

M. S. J. B. Michael and D. Wijesekera. Forensic Capabilities For Service-Oriented Architectures. Technical report, 2008.

[19]

D. Larson and J. Liu. A new security metric for soa implementations. In Software Security and Reliability-Companion (SERE-C), 2013 IEEE 7th International Conference on, pages 102--108, June 2013.

Digital Library

[20]

M. Makutsoane and A. Leonard. A conceptual framework to determine the digital forensic readiness of a cloud service provider. In Management of Engineering Technology (PICMET), 2014 Portland International Conference on, pages 3313--3321, July 2014.

[21]

A. Marrington, M. Branagan, and J. Smith. Forensic challenges in service oriented architectures. In 2007 Research Network for a Secure Australia: Security Technology Conference, pages 356--366, Melbourne University, Melbourne, Victoria, September 2007. Australian Homeland Security Research Centre.

[22]

G. Palmer and M. Corporation. A Road Map for Digital Forensic Research. Technical report, Nov. 2001.

[23]

M. Pollitt. Applying traditional forensic taxonomy to digital forensics. In I. Ray and S. Shenoi, editors, IFIP Int. Conf. Digital Forensics, volume 285 of IFIP, pages 17--26. Springer, 2008.

[24]

S. Raghavan. Digital forensic research: current state of the art. CSI Transactions on ICT, 1(1):91--114, 2013.

[25]

P. Robinson, N. Cook, and S. K. Shrivastava. Implementing fair non-repudiable interactions with web services. In EDOC, pages 195--206. IEEE Computer Society, 2005.

Digital Library

[26]

J. D. Roo. Euler yet another proof engine, 1999âĂŞ2013.

[27]

S. V. H. S. C. J. D. R. T. S. E. Ruben Verborgh, DÃűrthe Arndt and R. V. deWalle. The pragmatic proof: Hypermedia api composition and execution. Theory and Practice of Logic Programming, 2014.

[28]

K. Sansurooah. Taxonomy of computer forensics methodologies and procedures for digital evidence seizure. School of Computer and Information Science, Edith Cowan University, Perth, Western Australia, 2006.

[29]

Q. Z. Sheng, X. Qiao, A. V. Vasilakos, C. Szabo, S. Bourne, and X. Xu. Web services composition: A decadeâĂ&Zacute;s overview. Information Sciences, 280(0):218--238, 2014.

[30]

J. Sherwood, A. Clark, and D. Lynas. Enterprise Security Architecture: A Business-Driven Approach. CMP Books, 2005.

[31]

N. H. R. Y. M. F. A. Siti Rahayu Selamat, Shahrin Sahib. A forensic traceability index in digital forensic investigation. Journal of Information Security(JIS), 4:19--32, 2013.

[32]

S. Solms and C. Louwrens. The Relationship Between Digital Forensics, Corporate Governance, IT Governance, and IS Governance. IGI Global, 2006.

[33]

N. Thethi and A. Keane. Digital forensics investigations in the cloud. In Advance Computing Conference (IACC), 2014 IEEE International, pages 1475--1480, Feb 2014.

[34]

R. Vaarandi. Real-time classification of ids alerts with data mining techniques. In Military Communications Conference, 2009. MILCOM 2009. IEEE, pages 1--7, Oct 2009.

[35]

R. Vaarandi and K. Podins. Network ids alert classification with frequent itemset mining and data clustering. In CNSM, pages 451--456. IEEE, 2010.

[36]

R. Verborgh. Serendipitous Web Applications through Semantic Hypermedia. PhD thesis, Ghent University, Ghent, Belgium, Feb. 2014.

[37]

R. Verborgh, V. Haerinck, T. Steiner, D. V. Deursen, S. V. Hoecke, J. D. Roo, R. V. de Walle, and J. Gabarró. Functional composition of sensor web apis. In Proceedings of the 5th International Workshop on Semantic Sensor Networks, SSN12, Boston, Massachusetts, USA, November 12, 2012, pages 65--80, 2012.

[38]

J. Viinikka, H. Debar, L. Mé, and R. Séguier. Time series modeling for ids alert management. In Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security, ASIACCS '06, pages 102--113, New York, NY, USA, 2006. ACM.

Digital Library

[39]

M. Yacoub and Y. Bennani. Hvs: A heuristic for variable selection in multilayer artificial neural network classifier. In International Conference on Artificial Neural Networks and Intelligent Engineering, ANNIE '97, Missouri, USA, pages 527--532. Morgan Kaufmann, 1997.

Cited By

Rouached MAkremi AMacherki MKraiem N(2024)Policy-Based Smart Contracts Management for IoT Privacy PreservationFuture Internet10.3390/fi1612045216:12(452)Online publication date: 3-Dec-2024
https://doi.org/10.3390/fi16120452
Rouached M(2021)Web Service Composition SecurityInternational Journal of Service Science, Management, Engineering, and Technology10.4018/IJSSMET.202105010912:3(154-174)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.4018/IJSSMET.2021050109
Akremi A(2021)An Adaptative and Compliant Forensics Admissibility Metrics Generation MethodologyThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487734(495-503)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3487664.3487734
Show More Cited By

Index Terms

Forensics-aware web services composition and ranking
1. Information systems
  1. Information retrieval
  2. Information storage systems

Recommendations

Composition and evaluation of trustworthy web services

Web service technology seamlessly enables integration of different software to fulfil dynamic business demands in a platform-neutral fashion. By means of standard interfaces, service requesters can obtain their desired functionalities through certain ...
QoS-aware web services composition using GRASP with Path Relinking

In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added value logic as a service. When several alternative service providers are available for composition, quality of service (QoS) properties ...
Privacy-Aware Web Service Composition and Ranking

Service selection is a key issue in the Future Internet, where applications are built by composing services and content offered by different service providers. Most existing service selection schemas only focus on QoS properties of services such as ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services

December 2015

704 pages

ISBN:9781450334914

DOI:10.1145/2837185

General Chair:
Gabriele Anderst-Kotsis
Johannes Kepler University Linz, Austria
,
Program Chair:
Maria Indrawan-Santiago
Monash University, Australia

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 December 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

KACST

Conference

iiWAS '15

iiWAS '15: The 17th International Conference on Information Integration and Web-based Application & Services

December 11 - 13, 2015

Brussels, Belgium

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
73
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 09 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Rouached MAkremi AMacherki MKraiem N(2024)Policy-Based Smart Contracts Management for IoT Privacy PreservationFuture Internet10.3390/fi1612045216:12(452)Online publication date: 3-Dec-2024
https://doi.org/10.3390/fi16120452
Rouached M(2021)Web Service Composition SecurityInternational Journal of Service Science, Management, Engineering, and Technology10.4018/IJSSMET.202105010912:3(154-174)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.4018/IJSSMET.2021050109
Akremi A(2021)An Adaptative and Compliant Forensics Admissibility Metrics Generation MethodologyThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487734(495-503)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3487664.3487734
Akremi ASallay HRouached MBouaziz R(2020)Applying Digital Forensics to Service Oriented ArchitectureInternational Journal of Web Services Research10.4018/IJWSR.202001010217:1(17-42)Online publication date: Jan-2020
https://doi.org/10.4018/IJWSR.2020010102
Akremi ASriti MSallay HRouached M(2020)Ontology-Based Smart Sound Digital Forensics Analysis for Web ServicesDigital Forensics and Forensic Investigations10.4018/978-1-7998-3025-2.ch033(497-520)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-3025-2.ch033
Akremi ASriti MSallay HRouached M(2019)Ontology-Based Smart Sound Digital Forensics Analysis for Web ServicesInternational Journal of Web Services Research10.4018/IJWSR.201901010416:1(70-92)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJWSR.2019010104

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten