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Mining for Causal Relationships: A Data-Driven Study of the Islamic State

Published: 10 August 2015 Publication History

Abstract

The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014. In this paper, we present a data-driven approach to analyzing this group using a dataset consisting of 2200 incidents of military activity surrounding ISIS and the forces that oppose it (including Iraqi, Syrian, and the American-led coalition). We combine ideas from logic programming and causal reasoning to mine for association rules for which we present evidence of causality. We present relationships that link ISIS vehicle-bourne improvised explosive device (VBIED) activity in Syria with military operations in Iraq, coalition air strikes, and ISIS IED activity, as well as rules that may serve as indicators of spikes in indirect fire, suicide attacks, and arrests.

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  • (2023)DOMINO: Visual Causal Reasoning With Time-Dependent PhenomenaIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.320792929:12(5342-5356)Online publication date: Dec-2023
  • (2021)IntroductionIdentification of Pathogenic Social Media Accounts10.1007/978-3-030-61431-7_1(1-7)Online publication date: 5-Jan-2021
  • (2020)A Logic Programming Approach to Predict Enterprise-Targeted CyberattacksData Science in Cybersecurity and Cyberthreat Intelligence10.1007/978-3-030-38788-4_2(13-32)Online publication date: 6-Feb-2020
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    cover image ACM Conferences
    KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2015
    2378 pages
    ISBN:9781450336642
    DOI:10.1145/2783258
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 10 August 2015

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    Author Tags

    1. causalit
    2. rule learning
    3. security informatics

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    • Arizona State University

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    KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    Cited By

    View all
    • (2023)DOMINO: Visual Causal Reasoning With Time-Dependent PhenomenaIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.320792929:12(5342-5356)Online publication date: Dec-2023
    • (2021)IntroductionIdentification of Pathogenic Social Media Accounts10.1007/978-3-030-61431-7_1(1-7)Online publication date: 5-Jan-2021
    • (2020)A Logic Programming Approach to Predict Enterprise-Targeted CyberattacksData Science in Cybersecurity and Cyberthreat Intelligence10.1007/978-3-030-38788-4_2(13-32)Online publication date: 6-Feb-2020
    • (2019)Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social MediaCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316500(154-161)Online publication date: 13-May-2019
    • (2019)Reasoning About Future Cyber-Attacks Through Socio-Technical Hacking Information2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2019.00030(157-164)Online publication date: Nov-2019
    • (2018)Finding Cryptocurrency Attack Indicators Using Temporal Logic and Darkweb Data2018 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI.2018.8587361(91-93)Online publication date: Nov-2018
    • (2018)Early Identification of Pathogenic Social Media Accounts2018 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI.2018.8587339(169-174)Online publication date: Nov-2018
    • (2018)DARKMENTION: A Deployed System to Predict Enterprise-Targeted External Cyberattacks2018 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI.2018.8587334(31-36)Online publication date: Nov-2018
    • (2018)Finding Novel Event Relationships in Temporal Data2018 1st International Conference on Data Intelligence and Security (ICDIS)10.1109/ICDIS.2018.00009(9-16)Online publication date: Apr-2018
    • (2017)Big Data and CausalityAnnals of Data Science10.1007/s40745-017-0122-35:2(133-156)Online publication date: 1-Aug-2017

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