On fighting fire with fire: Strategic destabilization of terrorist networks

Vahid Behzadan, Amin Nourmohammadi, Mehmet Gunes, Murat Yuksel

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Scopus citations

    Abstract

    Terrorist organizations have social networks that enable them to recruit and operate around the world. This paper presents a novel computational framework for derivation of optimal destabilization strategies against dynamic social networks of terrorists. We develop a game-theoretic model to capture the distributed and complex dynamics of terrorist organizations, and introduce a technique for estimation of such dynamics from incomplete snapshots of target networks. Furthermore, we propose a mechanism for devising the optimal sequence of actions that drive the internal dynamics of targeted organizations towards an arbitrary state of instability. The performance of this framework is evaluated on a model of the Al-Qaeda network in 2001, verifying the efficacy of our proposals for counter-terrorism applications.

    Original languageEnglish
    Title of host publicationProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
    EditorsJana Diesner, Elena Ferrari, Guandong Xu
    Pages1120-1127
    Number of pages8
    ISBN (Electronic)9781450349932
    DOIs
    StatePublished - 31 Jul 2017
    Event9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
    Duration: 31 Jul 20173 Aug 2017

    Publication series

    NameProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

    Conference

    Conference9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
    Country/TerritoryAustralia
    CitySydney
    Period31/07/173/08/17

    Keywords

    • Counter terrorism
    • Dynamic network analysis
    • Guided self-organization
    • Network formation game
    • Reinforcement learning

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