Abstract
This paper presents a model and system architecture for an early warning system to detect terrorist threats. The paper discusses the shortcomings of state-of-the-art systems and outlines the functional requirements that must to be met by an ideal system working in the counterterrorism domain. The concept of generation of early warnings to predict terrorist threats is presented. The model relies on data collection from open data sources, information retrieval, information extraction for preparing structured workable data sets from available unstructured data, and finally detailed investigation. The conducted investigation includes social network analysis, investigative data mining, and heuristic rules for the study of complex covert networks for terrorist threat indication. The presented model and system architecture can be used as a core framework for an early warning system.
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References
Carpenter, M. A., and Stajkovic, A. D. 2006. Social Network Theory and Methods as Tools for Helping Business Confront Global Terrorism: Capturing the Case and Contingencies Presented by Dark Social Networks. In G. Suder (Ed), Corporate Strategies Under International Terrorism and Adversity, 7–19. UK: Edward Elgar Publishing.
Carley K.M. et al. 2006. Toward an Interoperable Dynamic Analysis Toolkit. Decision Support Systems 43(4): 1324–1347.
Chen H. et al. 2008, Terrorism informatics: Knowledge Management and Data mining for Homeland Security, Springer.
Chen H. et al. 2008. IEDs in the Dark Web: Genre Classification of Improvised Explosive Device Web Pages. In proc. IEEE ISI 2008.
Devlin, K., and Lorden, G. 2007. The Numbers Behind NUMB3RS: Solving Crime with Mathematics. Plume.
Engelbart, D. C. 1962. Augmenting Human Intellect: A Conceptual Framework, Summary Report. AFOSR-3233, Stanford Research Institute.
Gloor, P. A., and Zhao, Y. 2006. Analyzing Actors and Their Discussion Topics by Semantic Social Network Analysis. Information Visualization. IV 2006, pp. 130–135.
Memon, N., Wiil, U. K., Alhajj, R., Atzenbeck, C., and Harkiolakis, N. 2010. Harvesting Covert Networks: The Case Study of the iMiner Database. Accepted for the International Journal of Networking and Virtual Organizations (IJNVO). InderScience (to appear).
Memon N.; Larsen H.L.; Hicks D.; Harkiolakis N. 2008. Detecting Hidden Hierarchy in Terrorist Networks: Some Case Studies: Lecture Notes in Computer Science, vol. 5075/2008, pp. 477–489.
Memon N.; Hicks D.; Larsen H. L.; Uqaili M.A. 2007. Understanding the Structure of Terrorist Networks, In International Journal of Business Intelligence and Data Mining, vol. 2(4), pp. 401–425.
Memon N. 2007 “Investigative Data Mining: Analyzing, Visualizing and Destabilizing Terrorist Networks”, PhD dissertation, Aalborg University.
Memon N.; Hicks D.; Larsen H. L. 2006. How Investigative Data Mining Can Help Intelligence Agencies to Discover Dependence of Nodes in Terrorist Networks. Lecture Notes in Computer Science, vol. 4632/2006, pp. 430–441.
Steele, R. D. 2006. The Failure of 20th Century Intelligence, c
Shipman, F. M., Hsieh, H, Maloor, P., and Moore, J. M. 2001. The Visual Knowledge Builder: A Second Generation Spatial Hypertext, In Proc. of the ACM Hypertext Conference, pp. 113–122. ACM Press.
Thomas, J., and Cook, K. 2006. A Visual Analytics Agenda. IEEE Computer Graphics and Applications 26(1), 10–13.
Tsvelovat M., and Carley K. M. 2005. Structural Knowledge and Success of Antiterrorist Activity: The Downside of Structural Equivalence. Journal of social structures 6(2).
Wiil U.K., Memon, N., Gniadek J. 2009. Knowledge Management Processes, Tools and Techniques for Counterterrorism. International Conference on Knowledge Management and Information Sharing (KMIS 2009), (Funchal, Portugal, October). INSTICC Press, pp. 29–36.
Xu J., Chen H. (2005) “CrimeNet Explorer: A framework for criminal network knowledge discovery”, ACM Transactions on Information Systems, Vol. 23(2), pp. 201–226.
Damljanovic D., Agatonovic M., Cunningham H. 2010.: Natural Language Interfaces to Ontologies: Combining Syntactic Analysis and Ontology-based Lookup through the User Interaction. In Proceedings of the 7th Extended Semantic Web Conference (ESWC 2010), Springer Verlag, Heraklion, Greece, May 31–June 3, 2010.
Maynard D., Funk A., Peters W. 2009. Using Lexico-Syntactic Ontology Design Patterns for ontology creation and population. In Proceedings of ISWC Workshop on Ontology Patterns (WOP 2009), Washington, 2009.
Ning Li, Jun Rao, Eugene Shekita. 2009. Leveraging a scalable Row Store to Build a Distributed Text Index. In Proceedings of the first international workshop on Cloud data management (CloudDB 09) Hong Kong, China, pp. 29–36.
Qureshi P.A.R., Memon N., Wiil U. K. 2010. EWaS: Novel Approach for Generating Early Warnings to Prevent Terrorist Attacks in the Proceedings of 2010 Second International Conference on Computer Engineering and Application (ICCEA 2010), Bali Island, Indonesia, IEEE Computer Society, pp. 410–414.
Qureshi P. A. R., Wiil U. K., Memon N. 2009. Modeling Early Warning System to Predict Terrorist Threats: Preliminary Results. In the Proceedings of the 10th International Symposium on Knowledge and System Sciences, Hong Kong, China, 2009. pp. 179–190.
Memon N., Wiil U.K., Qureshi P. A. R. 2009. Design and Development of an Early Warning System to Prevent Terrorist Attacks. In the Proceedings of International Conference on Artificial Intelligence and Neural Networks, Beijing, China, 2009. IEEE Press, pp. 222–226
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Qureshi, P.A.R., Memon, N., Wiil, U.K. (2010). EWAS: Modeling Application for Early Detection of Terrorist Threats. In: Memon, N., Alhajj, R. (eds) From Sociology to Computing in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0294-7_8
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DOI: https://doi.org/10.1007/978-3-7091-0294-7_8
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