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Real Time Big Data Analytics for Predicting Terrorist Incidents.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Real Time Big Data Analytics for Predicting Terrorist Incidents./
Author:	Toure, Ibrahim.
Description:	1 online resource (142 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: A.
Contained By:	Dissertation Abstracts International79-02A(E).
Subject:	Information science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355237924

Real Time Big Data Analytics for Predicting Terrorist Incidents.
Toure, Ibrahim.

Real Time Big Data Analytics for Predicting Terrorist Incidents. - 1 online resource (142 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: A.

Thesis (Ph.D.)

Includes bibliographical references

Terrorism is a complex and evolving phenomenon. In the past few decades, we have witnessed an increase in the number of terrorist incidents in the world. The security and stability of many countries is threatened by terrorist groups. Perpetrators now use sophisticated weapons and the attacks are more and more lethal. Currently, terrorist incidents are highly unpredictable which allows terrorist groups to attack by surprise. The unpredictability of attacks is partly due to the lack of real time terrorism data collection systems, adequate risk models, and prediction methodologies. To bridge the gap between terrorist incidents and counter-terrorism measures, it is crucial to develop real time terrorism data collection systems along with novel and proven risk models and prediction methodologies. In this research, we developed a set of systems and methodologies to collect and analyze terrorism related data. The methodologies include terrorism data summarization for root cause analysis, cluster analysis of terrorist attacks into groups with similar patterns, a novel risk model that uses data collected via our data collection system, and a prediction method that uses our risk model and Markov Chains.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355237924Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Real Time Big Data Analytics for Predicting Terrorist Incidents.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: A.
500 $a Adviser: Aryya Gangopadhyay.
502 $a Thesis (Ph.D.) $c University of Maryland, Baltimore County $d 2017.
504 $a Includes bibliographical references
520 $a Terrorism is a complex and evolving phenomenon. In the past few decades, we have witnessed an increase in the number of terrorist incidents in the world. The security and stability of many countries is threatened by terrorist groups. Perpetrators now use sophisticated weapons and the attacks are more and more lethal. Currently, terrorist incidents are highly unpredictable which allows terrorist groups to attack by surprise. The unpredictability of attacks is partly due to the lack of real time terrorism data collection systems, adequate risk models, and prediction methodologies. To bridge the gap between terrorist incidents and counter-terrorism measures, it is crucial to develop real time terrorism data collection systems along with novel and proven risk models and prediction methodologies. In this research, we developed a set of systems and methodologies to collect and analyze terrorism related data. The methodologies include terrorism data summarization for root cause analysis, cluster analysis of terrorist attacks into groups with similar patterns, a novel risk model that uses data collected via our data collection system, and a prediction method that uses our risk model and Markov Chains.
520 $a Our methodology for root cause analysis utilizes our novel algorithm, along with Latent Dirichlet Allocation and historical terrorism data of START. Our clustering method segregates terrorist groups based on their similarities in attack patterns. Our data collection system is an automated crawler engine that collects data from selected data sources via RSS Feeds and on demand. The result is real time data that gets preprocessed automatically using selected keywords. Our novel terrorism risk model utilizes the preprocessed data to calculate terrorism risk levels at different locations. Lastly our prediction method utilizes our terrorism risk model, and Markov Chain models to predict future terrorist incidents in different countries. We have implemented a fully automated system that does not require any manual interventions for collecting and calculating the risk values. The results obtained in this research show a promising terrorism prediction system which predicts future attacks up to three months prior to the occurrence of an attack with a maximum of 96.85% precision and 96.32% recall. Our software system and methodologies can be a useful tool for terrorism analysts to improve counter-terrorism measures, and potentially prevent future terrorist attacks.
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538 $a Mode of access: World Wide Web
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