Nowadays, many resources for counter-terrorism operations are available for researchers belonging to different areas. In particular, the START project provides the Global Terrorism Database (GTD) that can be analyzed in order to provide, for instance, prediction models. The main idea underlying this work is using the historical data provided by GTD, which offers information related to terrorist attacks perpetrated since 1970, in order to conceptualize the behaviors of terrorist groups in specific time intervals. Such conceptualizations are, subsequently, used to understand the similarity between terrorist groups and elicit relations to represent terrorists' networks. The above networks can be used to study the temporal evolutions of terrorist groups' behaviors by applying the approach in different time periods along the timeline and studying differences among the resulting networks. The approach is mainly based on Rough Set Theory and Three-way Decisions Theory and provides an original similarity function based on the definition of boundary regions. (C) 2019 Elsevier B.V. All rights reserved.
Understanding the composition and evolution of terrorist group networks: A rough set approach
Loia V.;Orciuoli F.
2019-01-01
Abstract
Nowadays, many resources for counter-terrorism operations are available for researchers belonging to different areas. In particular, the START project provides the Global Terrorism Database (GTD) that can be analyzed in order to provide, for instance, prediction models. The main idea underlying this work is using the historical data provided by GTD, which offers information related to terrorist attacks perpetrated since 1970, in order to conceptualize the behaviors of terrorist groups in specific time intervals. Such conceptualizations are, subsequently, used to understand the similarity between terrorist groups and elicit relations to represent terrorists' networks. The above networks can be used to study the temporal evolutions of terrorist groups' behaviors by applying the approach in different time periods along the timeline and studying differences among the resulting networks. The approach is mainly based on Rough Set Theory and Three-way Decisions Theory and provides an original similarity function based on the definition of boundary regions. (C) 2019 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
