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A Cyberspace Ontology Model Under Non-cooperative Conditions

  • Jinkui YaoEmail author
  • Yulong Zhao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)

Abstract

The activities of cyberspace are not always cooperative, and confrontational behavior under non-cooperative conditions has even occurred at the beginning of the Internet and will persist. To describe the non-cooperative situation, we construct an ontology model to depict the entities and relations in the cyberspace. We divide the cyberspace into physical, logical, and social domains, and then build a conceptual model. According to the ontology modeling method, the upper layer, the domain and the application ontology are hierarchically constructed. Based on the Semantic Web Rule Language (SWRL), a reasoning framework is initially constructed to implement basic logical reasoning. We map the structured data in the data source to the model according to the predefined rules file, and extract the unstructured data to obtain the structured data. In order to verify the validity of the model, we designed a prototype system to integrate multi-source heterogeneous data and to achieve efficient query and reasoning.

Keywords

Cyberspace model Ontology Ontology modeling Non-cooperative conditions Ontology reasoning 

References

  1. 1.
    Schmidt-Schauß, M., Smolka, G.: Attributive concept descriptions with complements. Artif. Intell. 48(1), 1–26 (1991)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Horrocks, I., Sattler, U., Tobies, S.: Practical reasoning for expressive description logics. In: Ganzinger, H., McAllester, D., Voronkov, A. (eds.) LPAR 1999. LNCS, vol. 1705, pp. 161–180. Springer, Heidelberg (1999).  https://doi.org/10.1007/3-540-48242-3_11CrossRefzbMATHGoogle Scholar
  3. 3.
    Horrocks, I., Kutz, O., Sattler, U.: The even more irresistible SROIQ. In: Tenth International Conference on Principles of Knowledge Representation and Reasoning, pp. 57–67 (2006)Google Scholar
  4. 4.
    Horrocks, I.: Using an expressive description logic: FaCT or fiction? Knowl. Reasoning 98, 636–645 (1998)Google Scholar
  5. 5.
    Serafini, L., Tamilin, A.: DRAGO: distributed reasoning architecture for the semantic web. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 361–376. Springer, Heidelberg (2005).  https://doi.org/10.1007/11431053_25CrossRefGoogle Scholar
  6. 6.
    Tsarkov, D., Horrocks, I.: FaCT++ description logic reasoner: system description. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS, vol. 4130, pp. 292–297. Springer, Heidelberg (2006).  https://doi.org/10.1007/11814771_26CrossRefGoogle Scholar
  7. 7.
    Glimm, B., et al.: HermiT: an OWL 2 reasoner. J. Autom. Reasoning 53(3), 245–269 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Cuppens, F., Ortalo, R.: LAMBDA: a language to model a database for detection of attacks. In: Debar, H., Mé, L., Wu, S.F. (eds.) RAID 2000. LNCS, vol. 1907, pp. 197–216. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-39945-3_13CrossRefGoogle Scholar
  9. 9.
    Deng, Z., et al.: An extensible description model of cyber war system. J. Natl. Univ. Defense Technol. 36(01), 184–190 (2014)Google Scholar
  10. 10.
    Njilla, L.Y., et al.: Game theoretic modeling of security and trust relationship in cyberspace. Int. J. Commun Syst 29(9), 1500–1512 (2016)CrossRefGoogle Scholar
  11. 11.
    Barford, P., et al.: Cyber SA: situational awareness for cyber defense. In: Jajodia, S., Liu, P., Swarup, V., Wang, C. (eds.) Cyber Situational Awareness. ADIS, vol. 46, pp. 3–13. Springer, Boston (2010).  https://doi.org/10.1007/978-1-4419-0140-8_1CrossRefGoogle Scholar
  12. 12.
    Rashid, A., Sharma, D., Lone, T.A., Gupta, S., Gupta, S.K.: Secure communication in UAV assisted HetNets: a proposed model. In: Wang, G., Feng, J., Bhuiyan, M.Z.A., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11611, pp. 427–440. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-24907-6_32CrossRefGoogle Scholar
  13. 13.
    Bilar, D., Saltaformaggio, B.: Using a novel behavioral stimuli-response framework to defend against adversarial cyberspace participants. In: 2011 3rd International Conference on Cyber Conflict, pp. 1–16. IEEE (2011)Google Scholar
  14. 14.
    Vishik, C., Balduccini, M.: Making sense of future cybersecurity technologies: using ontologies for multidisciplinary domain analysis. In: Reimer, H., Pohlmann, N., Schneider, W. (eds.) ISSE 2015, pp. 135–145. Springer, Wiesbaden (2015).  https://doi.org/10.1007/978-3-658-10934-9_12CrossRefGoogle Scholar
  15. 15.
    Vorobiev, A., Bekmamedova, N.: An ontology-driven approach applied to information security. J. Res. Pract. Inf. Technol. 42(1), 61–76 (2010)Google Scholar
  16. 16.
    Doynikova, E., et al.: Ontology of metrics for cyber security assessment. In: Proceedings of the 14th International Conference on Availability, Reliability and Security, p. 52. ACM (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Jiangnan Institute of Computing TechnologyWuxiChina

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