Conceptual terrorist attacks classification : pre-processing for artificial intelligence-based classification

  • Oussama Kebir ,
  • Issam Nouaouri ,
  • Lilia Rejeb ,
  • Lamjed Ben Said  
  • a,c,d University of Tunis Institut Supérieur de Gestion de Tunis, SmartLab, Tunisia
  • Univ. Artois, UR 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), Béthune, 62400, France
Cite as
Kebir O., Nouaouri I., Rejeb L., Ben Said L. (2021). Conceptual terrorist attacks classification :
pre-processing for artificial intelligence-based classification. Proceedings of the 11th International Defence and Homeland Security Simulation Worskhop (DHSS 2021), pp. 16-24. DOI:
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The objective on this paper is to propose a conceptual classification for terrorist attacks that presents a preprocessing for Artificial Intelligence classification model based on two layers. The AI classification intends, firstly, to predict the parameters’ values from the Global Terrorism Database by a semi-supervised learning with multi-layer perceptron, and, secondly, to treat uncertainties on the predicted parameters using a fuzzy set system. Our proposed conceptual  classification is based on three criteria characterizing the terrorism violence, which are (1) threat, (2) vulnerability, and (3) consequences. Hence, to be able to determine the class of a given attack, we should quantify each criterion. To do so, we study, in this paper, the range of possible values of the various influencing parameters. Therefore, every criterion could be assigned to the three qualitative values: (a) High, (b) Medium, and (c) Low.


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