Segregate Malicious Behavior and Profit Maximization In Cross Organisational Business Process-As-A-Service (COBPAAS) Event Logs

Authors(2) :-G.Sasikala, V. Ramesh

As of late, web based shopping incorporating third-party payment platforms (TPPs) acquaints new security challenges due with complex communications between Application Programming Interfaces (APIs) of Merchants and TPPs. Malicious users may misuse security vulnerabilities by calling APIs in a subjective request or assuming different roles. To manage the security issue in the beginning times of framework improvement, this paper introduces a formal strategy for displaying and check of web based shopping business forms with malicious behavior patterns method considered in light of Petri nets. We propose a formal model called E- commerce Business Process Net to display a typical internet shopping business process that speak to expected capacities, and malicious behavior patterns representing to a potential attack that violates the security goals at the requirement examination stage. We build up an organized procedure that applies formal strategies while coordinating users through determining value-based prerequisites and choosing configurable highlights. The Binary Decision Diagram (BDD) analysis is then used to confirm that chose configurable features don't damage any limitations. At last, demonstrate checking is connected to confirm the arranged administration against the transactional requirement set we analyze whether a web based shopping business process is impervious to the known malicious behavior patterns. Process mining goes for transforming such event data into significant, noteworthy knowledge, so process execution or consistence issues can be recognized and rectified. Diverse process mining systems are accessible. These incorporate methods for mechanized process disclosure, conformance checking, execution mining and process variation analysis.

Authors and Affiliations

Assistant Professor, PG & Research Department of Computer Science and Applications, Adhiparasakthi College of Arts and Science (Autonomous), G.B.Nagar, Kalavai, Vellore, Tamil Nadu, India
V. Ramesh
M.Phil (CS) Research Scholar, PG & Research Department of Computer Science and Applications, Adhiparasakthi College of Arts and Science (Autonomous), G.B.Nagar, Kalavai, Vellore, Tamil Nadu, India

Verification, Transactional Requirements, Model Checking, Business Process Management, Process Mining, Infrequent Behaviour.

  1. A. Adriansyah. Aligning Observed and Modeled Behaviour. PhD thesis, Technische Universiteit Eindhoven, 2014.
  2. A. Adriansyah, J. Munoz-Gama, J. Carmona, B.F. van Dongen, and W.M.P. van der Aalst. Alignment based precision checking. In Proc. of BPM Workshops, pages 137–149, 2012.
  3. A. Adriansyah, B.F. van Dongen, and W.M.P. van der Aalst. Conformance checking using cost-based fitness analysis. In Proc. of EDOC, pages 55–64, 2011.
  4. C.C. Aggarwal. Outlier Analysis. Springer, 2013.
  5. S. Basu and M. Meckesheimer. Automatic outlier detection for time series: an application to sensor data. KAIS, 11(2):137–154, 2006.
  6. S. Budalakoti, A.N. Srivastava, and M.E. Otey. Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety. IEEE TSMCS, 39(1):101–113, Jan 2009.
  7. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection for discrete sequences: A survey. IEEE TKDE, 24(5):823–839, May 2012.
  8. R. Conforti, M. Dumas, L. Garc´?a-Banuelos, and M. La Rosa. Beyond ˜ tasks and gateways: Discovering BPMN models with subprocesses, boundary events and activity markers. In Proc. of BPM, pages 101–117, 2014.
  9. K. Das, J. Schneider, and D.B. Neill. Anomaly pattern detection in categorical datasets. In Proc. of ACM SIGKDD, pages 169–176, 2008.
  10. G. Florez-Larrahondo, S.M. Bridges, and R. Vaughn. Efficient modelling of discrete events for anomaly detection using hidden markov models. In Proc. of ISC, pages 506–514, 2005.
  11. C.W. Gunther and W.M.P. van der Aalst. Fuzzy mining - adaptive process ¨ simplification based on multi-perspective metrics. In Proc. of BPM, pages 328–343, 2007.
  12. M. Gupta, C.C. Aggarwal, and J. Han. Finding top-k shortest path distance changes in an evolutionary network. In Proc. of SSTD, pages 130–148. Springer, 2011.
  13. M. Gupta, J. Gao, C.C. Aggarwal, and J. Han. Outlier detection for temporal data: A survey. IEEE TKDE, 26(9):2250–2267, 2014.
  14. M. Gupta, A. Mallya, S. Roy, J.H.D. Cho, and J. Han. Local Learning for Mining Outlier Subgraphs from Network Datasets, pages 73–81. 2014.
  15. R. Gwadera, M.J. Atallah, and W. Szpankowski. Reliable detection of episodes in event sequences. KAIS, 7(4):415–437, May 2005.
  16. S.A. Hofmeyr, S. Forrest, and A. Somayaji. Intrusion detection using sequences of system calls. J. Comput. Secur., 6(3):151–180, August 1998.
  17. R.M. Karp. Reducibility among combinatorial problems. In Proc. Of CCC, pages 85–103. Springer US, 1972.
  18. E. Keogh, J. Lin, S.-H. Lee, and H. van Herle. Finding the most unusual time series subsequence: algorithms and applications. KAIS, 11(1):1–27, 2006.
  19. E. Keogh, S. Lonardi, and B. Chiu. Finding surprising patterns in a time series database in linear time and space. In Proc. of ACM SIGKDD, pages 550–556, 2002.

Publication Details

Published in : Volume 4 | Issue 9 | July-August 2018
Date of Publication : 2018-07-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 322-329
Manuscript Number : IJSRSET184968
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

G.Sasikala, V. Ramesh, " Segregate Malicious Behavior and Profit Maximization In Cross Organisational Business Process-As-A-Service (COBPAAS) Event Logs , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.322-329, July-August-2018.
Journal URL :

Article Preview