Automated Reasoning over Provenance-Aware Communication Network Knowledge in Support of Cyber-Situational Awareness

  • Leslie F. SikosEmail author
  • Markus Stumptner
  • Wolfgang Mayer
  • Catherine Howard
  • Shaun Voigt
  • Dean Philp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)


Cyber-situational awareness is crucial to applications such as network monitoring and management, vulnerability assessment, and defense. To gain improved cyber-situational awareness, analysts can benefit from automated reasoning-based frameworks. However, such frameworks would require the processing of enormous amounts of network data, which are characterized by syntactic variability. The formal representation of networking concepts, their properties, and interrelations using RDF can narrow the interoperability gaps between routing information and network semantics. Formal knowledge representation also enables automated reasoning, which facilitates network knowledge discovery by making implicit statements explicit. However, capturing and reasoning over the provenance of RDF statements, which is essential to build analysts’ trust in automated support tools, is not trivial. This paper presents a novel framework for capturing provenance-aware network knowledge to enable automated reasoning for network applications that require cyber-situational awareness.


  1. 1.
    Kott, A., Wang, C., Erbacher, R.F. (eds.): Cyber Defense and Situational Awareness. AIS, vol. 62. Springer, Cham (2014). Scholar
  2. 2.
    Zhong, C., Yen, J., Liu, P., Erbacher, R.F., Garneau, C., Chen, B.: Studying analysts’ data triage operations in cyber defense situational analysis. In: Liu, P., Jajodia, S., Wang, C. (eds.) Theory and Models for Cyber Situation Awareness. LNCS, vol. 10030, pp. 128–169. Springer, Cham (2017). Scholar
  3. 3.
    Sikos, L.F.: Mastering Structured Data on the Semantic Web. Apress, Berkeley (2015). Scholar
  4. 4.
    Dapoigny, R., Barlatier, P.: Formal foundations for situation awareness based on dependent type theory. Inf. Fusion 14(1), 87–107 (2013). Scholar
  5. 5.
    Sikos, L.F.: Description Logics in Multimedia Reasoning. Springer, Cham (2017). Scholar
  6. 6.
    Ballora, M., Giacobe, N.A., McNeese, M., Hall, D.L.: Information data fusion and computer network defense. In: Onwubiko, C., Owens, T. (eds.) Situational awareness in computer network defense, pp. 141–164. IGI Global, Hershey (2012).
  7. 7.
    AlEroud, A., Karabatis, G.: A framework for contextual information fusion to detect cyber-attacks. In: Alsmadi, I.M., Karabatis, G., AlEroud, A. (eds.) Information Fusion for Cyber-Security Analytics. SCI, vol. 691, pp. 17–51. Springer, Cham (2017). Scholar
  8. 8.
    Wang, F., Hu, L., Zhou, J., Hu, J., Zhao, K.: A semantics-based approach to multi-source heterogeneous information fusion in the Internet of things. Soft. Comput. 21(8), 2005–2013 (2017). Scholar
  9. 9.
    Dividino, R., Sizov, S., Staab, S., Schueler, B.: Querying for provenance, trust, uncertainty and other meta knowledge in RDF. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 204–219 (2009). Scholar
  10. 10.
    Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. 194, 28–61 (2013). Scholar
  11. 11.
    Hartig, O., Thompson, B.: Foundations of an alternative approach to reification in RDF (2014).
  12. 12.
    Zimmermann, A., Lopes, N., Polleres, A., Straccia, U.: A general framework for representing, reasoning and querying with annotated Semantic Web data. Web Semant. Sci. Serv. Agents World Wide Web 11, 72–95 (2012). Scholar
  13. 13.
    Analyti, A., Damásio, C.V., Antoniou, G., Pachoulakis, I.: Why-provenance information for RDF, rules, and negation. Ann. Math. Artif. Intell. 70(3), 221–277 (2014). Scholar
  14. 14.
    Berners-Lee, T., Connolly, D., Kagal, L., Scharf, Y., Hendler, J.: N3Logic: a logical framework for the world wide web. Theory Pract. Log. Program. 8(3), 249–269 (2008). Scholar
  15. 15.
    Ding, L., Finin, T., Peng, Y., Da Silva, P., McGuinness, D.: Tracking RDF graph provenance using RDF molecules. Paper presented at Fourth International Semantic Web Conference, Galway, Ireland, 6–10 November 2005Google Scholar
  16. 16.
    Sahoo, S.S., Bodenreider, O., Hitzler, P., Sheth, A., Thirunarayan, K.: Provenance context entity (PaCE): scalable provenance tracking for scientific RDF data. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 461–470. Springer, Heidelberg (2010). Scholar
  17. 17.
    Nguyen, V., Bodenreider, O., Sheth, A.: Don’t like RDF reification? In: Chung, C.W., Broder, A., Shim, K., Shim, K. (eds.) Proceedings of the 23rd International Conference on World Wide Web. ACM, New York (2014).
  18. 18.
    Carroll, J.J., Bizer, C., Hayes, P., Stickler, P.: Named graphs, provenance and trust. In: Proceedings of the 14th International Conference on World Wide Web. ACM, New York (2005).
  19. 19.
    Flouris, G., Fundulaki, I., Pediaditis, P., Theoharis, Y., Christophides, V.: Coloring RDF triples to capture provenance. In: Bernstein, A., et al. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 196–212. Springer, Heidelberg (2009). Scholar
  20. 20.
    Sahoo, S.S., Sheth, A.: Provenir ontology: towards a framework for eScience provenance management. In: Microsoft eScience Workshop, Pittsburgh, PA, USA, 15–17 October 2009Google Scholar
  21. 21.
    Sikos, L.F. (ed.): AI in Cybersecurity. Springer, Cham (2018). Scholar
  22. 22.
    Martinez, A., Yannuzzi, M., López, J., Serral-Gracià, R., Ramirez, W.: Applying information extraction for abstracting and automating CLI-based configuration of network devices in heterogeneous environments. In: Laalaoui, Y., Bouguila, N. (eds.) Artificial Intelligence Applications in Information and Communication Technologies. SCI, vol. 607, pp. 167–193. Springer, Cham (2015). Scholar
  23. 23.
    ETSI: ETSI industry specification group: measurement ontology for IP traffic (MOI); requirements for IP traffic measurement ontologies development (2012)Google Scholar
  24. 24.
    Voigt, S., Howard, C., Philp, D., Penny, C.: Representing and reasoning about logical network topologies. In: Croitoru, M., Marquis, P., Rudolph, S., Stapleton, G. (eds.) GKR 2017. LNCS (LNAI), vol. 10775, pp. 73–83. Springer, Cham (2018). Scholar
  25. 25.
    Sikos, L.F., Stumptner, M., Mayer, W., Howard, C., Voigt, S., Philp, D.: Representing network knowledge using provenance-aware formalisms for cyber-situational awareness. Procedia Comput. Sci. (2018)Google Scholar
  26. 26.
    Doyle, J.: Routing TCP/IP, vol. 2, 2nd edn. Cisco Press, Indianapolis (2017)Google Scholar
  27. 27.
    Tadimety, P.R.: Link state advertisements. OSPF: A Network Routing Protocol, pp. 75–90. Apress, Berkeley (2015). Scholar
  28. 28.
    ter Horst, H.J.: Completeness, decidability and complexity of entailment for RDF Schema and a semantic extension involving the OWL vocabulary. Web Semant. Sci. Serv. Agents World Wide Web 3(2–3), 79–115 (2005). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of South AustraliaAdelaideAustralia
  2. 2.Defence Science and Technology GroupAdelaideAustralia

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