Advertisement

Ontology Based Anomaly Detection for File Integration

  • Özgü CanEmail author
  • İbrahim Uzum
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1057)

Abstract

File integration systems enable file transfers between different systems in order to automate routine business processes. Therefore, the standardization in data exchange between different organizations or decentralized subsidiaries of an organization is achieved. However, abnormal situations may occur during the file integration process. In order to protect the persistence of integration channels, the abnormal files must be detected. For this purpose, anomaly detection is used to trace integrations continuously and to detect abnormal files instantly. In this study, an ontology based anomaly detection approach is proposed in order to detect abnormal situations in real time file integration systems. Thus, a file integration that is achieved on an electronic system will be traced and information will be given to the system administrator if any abnormalities occur during the integration process. Therefore, an abnormal situation that can stop the current file flow on file integration systems will be detected.

Keywords

Anomaly detection Ontology Semantic web File integration 

References

  1. 1.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection : A Survey. J. ACM Comput. Surv. (CSUR) 41(3), 15 (2009). Article No. 15Google Scholar
  2. 2.
    Abdoli, F., Kahani, M.: Ontology-based distributed intrusion detection system. In: 14th International CSI Computer Conference (CSICC), pp. 65–70. IEEE, Tehran (2009)Google Scholar
  3. 3.
    Hsieh, C., Chen, R.-C., Huang, Y.-F.: Applying an ontology to a patrol intrusion detection system for wireless sensor networks. Int. J. Distrib. Sensor Netw. 10(1), 634748 (2014). 14 pagesCrossRefGoogle Scholar
  4. 4.
    Hung, S.-S., Liu, D.S.-M.: A user-oriented ontology-based approach for network intrusion detection. Comput. Stand. Interfaces 30(1–2), 78–88 (2008)CrossRefGoogle Scholar
  5. 5.
    Kolaczek, G., Juszczyszyn, K.: Attack pattern analysis framework for multiagent intrusion detection system. Int. J. Comput. Intell. Syst. 1(3), 215–224 (2008)Google Scholar
  6. 6.
    Pardo, E., Espes, D., Le-Parc, P.: A framework for anomaly diagnosis in smart homes based on ontology. Proc. Comput. Sci. 83, 545–552 (2016)CrossRefGoogle Scholar
  7. 7.
    Moustafa, N., Hua, J., Slay, J.: A holistic review of network anomaly detection systems: a comprehensive survey. J. Netw. Comput. Appl. 128, 33–55 (2019)CrossRefGoogle Scholar
  8. 8.
    Sarno, R., Sinaga, FP.: Business process anomaly detection using ontology-based process modelling and multi-level class association rule learning. In: International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 12–17. IEEE, Bandung (2015).  https://doi.org/10.1109/IC3INA.2015.7377738
  9. 9.
    Roy, J., Davenport, M.: Exploitation of maritime domain ontologies for anomaly detection and threat analysis. In: International WaterSide Security Conference, pp. 1–8. IEEE, Carrara (2010).  https://doi.org/10.1109/WSSC.2010.5730278
  10. 10.
    Vandecasteele, A., Napoli, A.: An enhanced spatial reasoning ontology for maritime anomaly detection. In: 7th International Conference on System of Systems Engineering, pp. 247–252. IEEE, Genoa (2012)Google Scholar
  11. 11.
    Gruber, T.R.: A translation approach to portable ontologies. Knowl. Acquis. 5(2), 199–220 (1993)CrossRefGoogle Scholar
  12. 12.
    SPARQL Query Language for RDF. https://www.w3.org/TR/rdf-sparql-query. Accessed 30 June 2019
  13. 13.
    Apache Jena. https://jena.apache.org. Accessed 30 June 2019

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringEge UniversityBornova-IzmirTurkey

Personalised recommendations