A Mark Based-Temporal Conceptual Graphs for Enhancing Big Data Management and Attack Scenario Reconstruction

  • Yacine Djemaiel
  • Boutheina A. Fessi
  • Noureddine Boudriga
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 208)


The management of big data is mainly affected by the size of the big graph data that represents the huge volumes of data. The size of this structure may increase with the size of data to be handled over the time. Facing this issue, the querying time may be affected and the introduced delay may not be tolerated by running applications. Moreover, the investigation of attacks through the collected massive data could not be ensured using traditional approaches, which do not support big data constraints. In this context, we propose in this paper, a novel temporal conceptual graph to represent the big data and to optimize the size of the derived graph. The proposed scheme built on this novel graph structure enables tracing back of attacks using big data. The efficiency of the proposed scheme for the reconstruction of attack scenarios is illustrated using a case study in addition to a conducted comparative analysis showing how smart big graph data is obtained through the optimization of the graph size.


Big data Smart data Temporal conceptual graph Attack scenario Investigation 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yacine Djemaiel
    • 1
  • Boutheina A. Fessi
    • 1
  • Noureddine Boudriga
    • 1
  1. 1.Communications Networks and Security Research Laboratory (CN&S)University of CarthageTunisTunisia

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