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Covert network analysis to detect key players using correlation and social network analysis

Published: 22 March 2017 Publication History

Abstract

The increasing terrorist events across all over the world have attracted the attention of many researchers towards counter-terrorism. This field demands from them to contribute in developing new techniques and methods for analysis, identification and prediction of terrorist events and group leaders. In this paper, we propose a model to detect key players from a network keeping focus on their communication contents. The proposed model finds correlation of communication contents of all nodes with data dictionary and detects nodes based on a threshold correlation value. A new network is drawn and its density is calculated. After that different centrality measures are applied on new network and most important nodes detected using each measure. That gives us different key players with different roles in the network. Data dictionary consists of words or terms used by the terrorist in their communication. We used Enron email data set to test and validate our proposed model.

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Cited By

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  • (2023)HURI: Hybrid user risk identification in social networksWorld Wide Web10.1007/s11280-023-01192-w26:5(3409-3439)Online publication date: 28-Jul-2023
  • (2021)Computational Robotics: An Alternative Approach for Predicting Terrorist NetworksInternational Journal of Robotics and Automation Technology10.31875/2409-9694.2021.08.18(1-11)Online publication date: 24-Nov-2021
  • (2019)Covert NetworksProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331749(628-637)Online publication date: 8-May-2019

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cover image ACM Other conferences
ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing
March 2017
1349 pages
ISBN:9781450347747
DOI:10.1145/3018896
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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Association for Computing Machinery

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Publication History

Published: 22 March 2017

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  1. key player
  2. preprocessing
  3. similarity measures

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ICC '17

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ICC '17 Paper Acceptance Rate 213 of 590 submissions, 36%;
Overall Acceptance Rate 213 of 590 submissions, 36%

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View all
  • (2023)HURI: Hybrid user risk identification in social networksWorld Wide Web10.1007/s11280-023-01192-w26:5(3409-3439)Online publication date: 28-Jul-2023
  • (2021)Computational Robotics: An Alternative Approach for Predicting Terrorist NetworksInternational Journal of Robotics and Automation Technology10.31875/2409-9694.2021.08.18(1-11)Online publication date: 24-Nov-2021
  • (2019)Covert NetworksProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331749(628-637)Online publication date: 8-May-2019

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