An Intelligent Threat Prevention Framework with Heterogeneous Information

WenJun Ma, Weiru Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
81 Downloads (Pure)


Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.
Original languageEnglish
Title of host publication21Sst European Conference on Artificial Intelligence (ECAI 2014)
Number of pages2
Publication statusPublished - Aug 2014
EventEuropean Conference on Artificial Intelligence (ECAI) - , Czech Republic
Duration: 18 Aug 201422 Aug 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Electronic)0922-6389


ConferenceEuropean Conference on Artificial Intelligence (ECAI)
Country/TerritoryCzech Republic


  • Reasoning under Uncertainty
  • Game theory
  • Security


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