Abstract
To advance the agenda in counterterrorism, this work demonstrates how analysts can combine unsupervised machine learning, exploratory data analysis, and statistical tests to discover features associated with different terrorist motives. A new empirical text mining method created a “motive” field in the Global Terrorism Database to enable associative relationship mining among features that characterize terrorist events. The methodology incorporated K-means co-clustering, three methods of non-linear projection, and two spatial association tests to reveal statistically significant relationships between terrorist motives, tactics, and targets. Planners and investigators can replicate the approach to distill knowledge from big datasets to help advance the state of the art in counterterrorism.
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References
Abrahms, M., & Conrad, J. (2017). The strategic logic of credit claiming: A new theory for anonymous terrorist attacks. Security Studies, 26(2), 279–304. https://doi.org/10.1080/09636412.2017.1280304
Adnan, M., & Rafi, M. (2015). Extracting patterns from global terrorist dataset (GTD) using co-clustering approach. Journal of Independent Studies and Research, 13(1), 7. https://doi.org/10.31645/jisrc/(2015).13.1.0002
Aggarwal, C. C. (2015). Data mining. Springer International Publishing.
Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Pearson.
Aleroud, A., & Gangopadhyay, A. (2018). Multimode co-clustering for analyzing terrorist networks. Information Systems Frontiers, 20(5), 1053–1074. https://doi.org/10.1007/s10796-016-9712-4
Ammar, J. (2019). Cyber Gremlin: Social networking, machine learning and the global war on Al-Qaida-and IS-inspired terrorism. International Journal of Law and Information Technology, 27(3), 238–265. https://doi.org/10.1093/IJLIT/EAZ006
Anselin, L. (1995). Local Indicators of Sspatial Association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Atsa’am, D. D., Wario, R., & Okpo, F. E. (2020). A new terrorism categorization based on casualties and consequences using hierarchical clustering. Journal of Applied Security Research. https://doi.org/10.1080/19361610.2020.1769461
Bayar, Y., & Gavriletea, M. (2018). Peace, terrorism and economic growth in Middle East and North African countries. Quality & Quantity, 52(5), 2373–2392. https://doi.org/10.1007/S11135-017-0671-8
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W., Ng, L. G., et al. (2019). Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology, 37(1), 38–44. https://doi.org/10.1038/nbt.4314
Campedelli, G., Bartulovic, M., & Carley, K. (2021). Learning future terrorist targets through temporal meta-graphs. Scientific Reports. https://doi.org/10.1038/s41598-021-87709-7
Campedelli, G., Cruickshank, I., & Carley, K. (2021). Multi-modal networks reveal patterns of operational similarity of terrorist organizations. Terrorism and Political Violence. https://doi.org/10.1080/09546553.2021.2003785
Campedelli, G., Cruickshank, I., & Carley, M. K. (2019). A complex networks approach to find latent clusters of terrorist groups. Applied Network Science. https://doi.org/10.1007/s41109-019-0184-6
Clauset, A., & Wiegel, F. W. (2010). A generalized aggregation-disintegration model for the frequency of severe terrorist attacks. Journal of Conflict Resolution, 54(1), 179–197. https://doi.org/10.1177/0022002709352452
Conlon, S., Abrahams, A., & Simmons, L. (2015). Terrorism information extraction from online reports. Journal of Computer Information Systems, 55(3), 20–28. https://doi.org/10.1080/08874417.2015.11645768
Curia, F. (2020). Unsupervised hybrid algorithm to detect anomalies for predicting terrorists attacks. International Journal of Computer Applications, 176(35), 975:8887. https://doi.org/10.5120/ijca2020920432
Ding, F., Ge, Q., Jiang, D., Fu, J., & Hao, M. (2017). Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach. PLoS ONE, 12(6), e0179057.
Enders, W., Parise, G. F., & Sandler, T. (1992). A time-series analysis of transnational terrorism: Trends and cycles. Defence and Peace Economics, 3(4), 305–320. https://doi.org/10.1080/10430719208404739
Feng, Y., Wang, D., Yin, Y., Li, Z., & Hu, Z. (2020). An XGBoost-based casualty prediction method for terrorist attacks. Complex & Intelligent Systems, 6, 1–20.
Guo, D., Liao, K., & Morgan, M. (2007). Visualizing patterns in a global terrorism incident database. Environment and Planning B: Planning and Design, 34(5), 767–784. https://doi.org/10.1068/b3305
Hao, M., Jiang, D., Ding, F., Fu, J., & Chen, S. (2019). Simulating spatio-temporal patterns of terrorism incidents on the Indochina peninsula with GIS and the random forest method. ISPRS International Journal of Geo-Information, 8(3), 133.
Heiser, W. J. (1985). Multidimensional scaling by optimizing goodness of fit to a smooth hypothesis. University of Leiden.
Huamaní, E. L., Alicia, A. M., & Roman-Gonzalez, A. (2020). Machine learning techniques to visualize and predict terrorist attacks worldwide using the global terrorism database. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/IJACSA.2020.0110474
Hung, B., Jayasumana, A., & Bandara, V. (2018). INSiGHT: A system to detect violent extremist radicalization trajectories in dynamic graphs. Data & Knowledge Engineering, 118, 52–70. https://doi.org/10.1016/J.DATAK.2018.09.003
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R (Vol. 112). Springer. https://doi.org/10.1007/978-1-4614-7138-7
Krasmann, S., & Hentschel, C. (2019). 'Situational awareness’: Rethinking security in times of urban terrorism. Security Dialogue, 50(2), 181–197. https://doi.org/10.1177/0967010618819598
LaFree, G. (2010). The Global Terrorism Database (GTD) Accomplishments and Challenges. Perspectives on Terrorism, 4(1), 24–46. Retrieved from http://www.jstor.org/stable/26298434
Loia, V., & Orciuoli, F. (2019). Understanding the composition and evolution of terrorist group networks: A rough set approach. Future Generation Computer Systems, 101, 983–992. https://doi.org/10.1016/j.future.2019.07.049
Lu, P., Zhang, Z., Li, M., Chen, D., & Yang, H. (2020). Agent-based modeling and simulations of terrorist attacks combined with Stampedes. Knowledge-Based Systems, 205, 106291. https://doi.org/10.1016/j.knosys.2020.106291
Maaten, L. V., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov 2008), 2579–2605.
Madeira, S. C., & Oliveira, A. L. (2004). Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1(1), 24–45. https://doi.org/10.1109/TCBB.2004.2
Martin, I. W. (2008). The permanent tax revolt: How the property tax transformed American politics. Stanford University Press.
Mashechkin, I. V., Petrovskiy, M. I., Tsarev, D. V., & Chikunov, M. N. (2019). Machine learning methods for detecting and monitoring extremist information on the internet. Programming and Computer Software, 45(3), 99–115. https://doi.org/10.1134/S0361768819030058
Miller, E. (2020). Global Terrorism Overview: Terrorism in 2019. College Park, Maryland: University of Maryland. https://www.start.umd.edu/pubs/START_GTD_GlobalTerrorismOverview2019_July2020.pdf
Mishra, N., Swagatika, S., & Singh, D. (2020). An intelligent framework for analysing terrorism actions using Cloud. In P. Srikanta, W. H. I. Andrew, T. Madjid, & J. Vipul (Eds.), New paradigm in decision science and management. Advances in intelligent systems and computing (Vol. 1005, pp. 225–235). Springer. https://doi.org/10.1007/978-981-13-9330-3_21
Naouali, S., Salem, S. B., & Chtourou, Z. (2020). Uncertainty mode selection in categorical clustering using the rough set theory. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113555
Nizamani, S., & Memon, N. (2012). Detecting terrorism incidence type from news summary. In T. Khine Soe (Ed.), Advanced information technology in education. Advances in intelligent and soft computing (Vol. 126, pp. 95–102). Springer. https://doi.org/10.1007/978-3-642-25908-1_14
Opiyo, O. P., Mukisa, M. T., & Ratemo, M. C. (2019). An evaluation of hybrid machine learning classifier models for identification of terrorist groups in the aftermath of an attack. International Research Journal of Engineering and Technology, 6(9), 1856–1864.
Pruyt, E., & Kwakkel, J. (2014). Radicalization under deep uncertainty: A multi-model exploration of activism, extremism, and terrorism. System Dynamics Review. https://doi.org/10.1002/sdr.1510
Python, A., Bender, A., Nandi, A., Hancock, P., Arambepola, R., Brandsch, J., & Lucas, T. (2021). Predicting non-state terrorism worldwide. Science Advances. https://doi.org/10.1126/sciadv.abg4778
Salem, S. B., & Naouali, S. (2016). Pattern recognition approach in multidimensional databases: Application to the global terrorism database. International Journal of Advanced Computer Science and Applications (IJACSA). https://doi.org/10.14569/IJACSA.2016.070838
Schaller, R. R. (1997). Moore’s law: Past, present and future. IEEE Spectrum, 34(6), 52–59. https://doi.org/10.1109/6.591665
Strang, K., & Sun, Z. (2017). Analyzing relationships in terrorism big data using Hadoop and statistics. Journal of Computer Information Systems, 57(1), 67–75. https://doi.org/10.1080/08874417.2016.1181497
Sun, A., Naing, M., Lim, E., & Lam, W. (2003). Using support vector machines for terrorism information extraction. In C. Hsinchun, M. Richard, D. Z. Daniel, D. Chris, S. Jenny, & M. Therani (Eds.), Intelligence and security informatics. ISI 2003. Lecture notes in computer science. (Vol. 2665). Springer. https://doi.org/10.1007/3-540-44853-5_1
Tolan, G. M., & Soliman, O. S. (2015). An experimental study of classification algorithms for terrorism prediction. International Journal of Knowledge Engineering-IACSIT, 1(2), 107–112.
Uddin, M. I., Zada, N., Aziz, F., Saeed, Y., Zeb, A., Shah, S. A., et al. (2020). Prediction of future terrorist activities using deep neural networks. Complexity. https://doi.org/10.1155/2020/1373087
USCB. (2019). TIGER/line shapefiles technical documentation. United States Census Bureau (USCB).
Venna, J., & Kaski, S. (2001). Neighborhood preservation in nonlinear projection methods: An experimental study. In D. Georg, B. Horst, & H. Kurt (Eds.), International conference on artificial neural networks. 2130 (pp. 485–491). Springer. https://doi.org/10.1007/3-540-44668-0_68
Wall, C. (2021). The (Non) Deus-Ex Machina: A realistic assessment of machine learning for countering domestic terrorism. Studies in Conflict and Terrorism. https://doi.org/10.1080/1057610X.2021.1987656
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Bridgelall, R. Applying unsupervised machine learning to counterterrorism. J Comput Soc Sc 5, 1099–1128 (2022). https://doi.org/10.1007/s42001-022-00164-w
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DOI: https://doi.org/10.1007/s42001-022-00164-w