Abstract
Followed by the 9/11 attacks in 2001 and the subsequent events, terrorism and other asymmetrical threat situations became increasingly important for security-related efforts of most western societies. In a similar period, the development of data gathering and analysis techniques especially using the methods of machine learning has made rapid progress. Aiming to utilize this development, this paper employs artificial neural networks for long-term time series prediction of terrorist event data. A major focus of the paper lies on the specific use of convolutional neural networks (CNNs) for this task, as well as the comparison to the performance of classical methods for (long-term) time series prediction. As the databases like Global Terrorism Database and Fraunhofer’s terrorist event database are not extensive enough to train a deep learning method, a simple toy model for the generation of time series data from one or more terrorist groups with defined properties is established. Metrics for comparison of the different approaches are collected and discussed, and a customized sliding-window metric is introduced. The study shows the principle applicability of CNNs for this task and offers constraints as well as possible extensions for future studies. Based on these results, continuation and further extension of data collection efforts and ML optimization techniques are encouraged.
Similar content being viewed by others
References
Akram M, El C (2016) Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. IJCA 143(11):7–11. https://doi.org/10.5120/ijca2016910497
Bengio Y, Lecun Y (1997) Convolutional networks for images, speech, and time-series. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, MA, pp 255–258
Bista I, Carvalho G, Walsh K, Seymour M, Hajibabaei M, Lallias D et al (2017) Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat Commun 8:14087. https://doi.org/10.1038/ncomms14087
Borovykh A, Bohte S, Oosterlee CW (2017) Conditional time series forecasting with convolutional neural networks. http://arxiv.org/pdf/1703.04691v5
Bowie, Neil G (2018) 30 terrorism databases and data sets: a new inventory. In: Perspectives on terrorism 12(5). http://bit.ly/2N3AnN4
Chen M, Chen B-T (2015) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci 294:227–241. https://doi.org/10.1016/j.ins.2014.09.038
Global Terrorism Database (GTD) (2018) https://www.start.umd.edu/gtd
Lee N-U, Shim J-S, Ju Y-W, Park S-C (2018) Design and implementation of the SARIMA–SVM time series analysis algorithm for the improvement of atmospheric environment forecast accuracy. Soft Comput 22(13):4275–4281. https://doi.org/10.1007/s00500-017-2825-y
Li Shuying, Zhuang Jun, Shen Shifei (2017) Dynamic forecasting conditional probability of bombing attacks based on time-series and intervention analysis. Risk Anal Off Publ Soc Risk Anal 37(7):1287–1297. https://doi.org/10.1111/risa.12679
Madan R, SarathiMangipudi P (2018) Predicting computer network traffic: a time series forecasting approach using DWT, ARIMA and RNN. In: 2018 eleventh international conference on contemporary computing (IC3). 2018 eleventh international conference on contemporary computing (IC3). Noida, 8/2/2018–8/4/2018. IEEE, pp 1–5
Robert N (2019) Statistical forecasting. Notes on regression and time series analysis. Fuqua School of Business, Duke University. http://people.duke.edu/~rnau/411home.htm
Sak H, Senior A, Rao K, Beaufays F (2015) Fast and accurate recurrent neural network acoustic models for speech recognition. https://arxiv.org/pdf/1507.06947. Accessed 24 July 2015
Serra E, Subrahmanian VS (2014) A survey of quantitative models of terror group behavior and an analysis of strategic disclosure of behavioral models. IEEE Trans Comput Soc Syst 1(1):66–88. https://doi.org/10.1109/TCSS.2014.2307454
Sheehan IS (2009) Has the global war on terror changed the terrorist threat? A time-series intervention analysis. Stud Confl Terror 32(8):743–761. https://doi.org/10.1080/10576100903039270
Shi H, Xu M, Li R (2018) Deep learning for household load forecasting—a novel pooling deep RNN. IEEE Trans Smart Grid 9(5):5271–5280. https://doi.org/10.1109/TSG.2017.2686012
Snehanshu S, Harsha A, Abu K, Aparna B (2017) Future terrorist attack prediction using machine learning techniques. https://doi.org/10.13140/RG.2.2.17157.96488
Wei L-Y (2016) A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl Soft Comput 42:368–376. https://doi.org/10.1016/j.asoc.2016.01.027
Zhang J-S, Xiao X-C (1999) Predicting chaotic time series using recurrent neural network. Chin Phys Lett 17(2):88. https://doi.org/10.1088/0256-307X/17/2/004
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jain, A.K., Grumber, C., Gelhausen, P. et al. A Toy Model Study for Long-Term Terror Event Time Series Prediction with CNN. Eur J Secur Res 5, 289–309 (2020). https://doi.org/10.1007/s41125-019-00061-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41125-019-00061-w