Pattern Recognition of Socio-technical Network Vulnerabilities – Modeling and preliminary results


  1. Léchevin, N.
  2. Jousselme, A.-L.
  3. Maupin, P.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN)
When faced with potentially disruptive events, the state of a network may unexpectedly evolve to regions of the state space where safe operating conditions are no longer ensured. It thus highly desirable to relate the network’s characteristics and operating conditions to its vulnerabilities, if any, in order to mitigate risk expressed as a function of network inoperability and loss of quality of service. A pattern recognition approach is adopted to relate the structural features of the network to the loss of operating nodes and edges. Two types of networks are considered in this document. A network characterized by flow conservation and capacity constraints is adapted from a fuse model, which may lead, in some instances, to cascading events. A tactical swarm of robots is deployed either to achieve terrain surveillance coverage or to maintain client connectivity so that every client can communicate in remote area. In both cases, the swarm of robots should maintain its connectivity at each time instant. The swarm deployment adapts to the loss of a robot caused by such factors as hardware/software failure, enemy action, or the presence of malware. The motion strategy prioritizes the client coverage, which may entail possible losses of connectivity. Given the motion strategy at hand, the swarm presents vulnerabilities related to the loss of some nodes. The classifier, instrumental in performing pattern recognition, is trained from a sample of networks obtained by some probabili

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Network;infrastructure;vulnerability;pattern recognition;prediction
Report Number
DRDC-VALCARTIER-TR-2013-409 — Technical Report
Date of publication
01 Dec 2013
Number of Pages
Electronic Document(PDF)

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