ATiPreTA: An analytical model for time-dependent prediction of terrorist attacks

• Autorzy: Kebir Oussama , Nouaouri Issam , Rejeb Lilia , Ben Said Lamjed

Identyfikatory

DOI

10.34768/amcs-2022-0036

• Języki publikacji: EN

Abstrakty

EN

In counter-terrorism actions, commanders are confronted with difficult and important challenges. Their decision-making processes follow military instructions and must consider the humanitarian aspect of the mission. In this paper, we aim to respond to the question: What would the casualties be if governmental forces reacted in a given way with given resources? Within a similar context, decision-support systems are required due to the variety and complexity of modern attacks as well as the enormous quantity of information that must be treated in real time. The majority of mathematical models are not suitable for real-time events. Therefore, we propose an analytical model for a time-dependent prediction of terrorist attacks (ATiPreTA). The output of our model is consistent with casualty data from two important terrorist events known in Tunisia: Bardo and Sousse attacks. The sensitivity and experimental analyses show that the results are significant. Some operational insights are also discussed.

Słowa kluczowe

EN

terrorist attack; attack classification; mathematical modeling; dynamic behavior simulation; damage prediction

PL

atak terrorystyczny; klasyfikacja ataku; model matematyczny; przewidywanie szkód

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 3
• Strony: 495--510
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Kebir Oussama

• SMART-LAB, Tunis Higher Institute of Management, University of Tunis, 92, Boulevard 9 Avril 1938, 1007 Tunis, Tunisia

autor

Nouaouri Issam

• LGI2A Laboratory, University of Artois, 9 rue du Temple, 62400 Béthune, France

autor

Rejeb Lilia

• SMART-LAB, Tunis Higher Institute of Management, University of Tunis, 92, Boulevard 9 Avril 1938, 1007 Tunis, Tunisia

autor

Ben Said Lamjed

• SMART-LAB, Tunis Higher Institute of Management, University of Tunis, 92, Boulevard 9 Avril 1938, 1007 Tunis, Tunisia

Bibliografia

• [1] Army, U.S. (2006). Field Manual 4-02.51: Combat and Operational Stress Control, Department of the Army, Washington DC.
• [2] Aylwin-Foster, N.R.F (2005). Changing the army for counterinsurgency operations, Military Review: 27–40.
• [3] Benesty, J., Chen, J., Huang, Y. and Cohen, I. (2009). Pearson correlation coefficient, in J. Cohen et al. (Eds), Noise Reduction in Speech Processing, Springer, Berlin/Heidelberg, pp. 1–4.
• [4] Bongers, A. and Torres, J.L. (2019). A bottleneck combat model: An application to the Battle of Thermopylae, Operational Research 21: 2859–2877, DOI: 10.1007/s12351-019-00513-0.
• [5] Chabir, K., Rhouma, T., Keller, J.Y. and Sauter, D. (2018). State filtering for networked control systems subject to switching disturbances, International Journal of Applied Mathematics and Computer Science 28(3): 473–482, DOI: 10.2478/amcs-2018-0036.
• [6] Chai, T. and Draxler, R.R. (2014). Root mean square error (RMSE) or mean absolute error (MAE), Geoscientific Model Development Discussions 7(1): 1525–1534.
• [7] Chmielewski, M., Kukie, M., Fr, D., Kukiełka, M., Frąszczak, D. and Bugajewski, D. (2018). Military and crisis management decision support tools for situation awareness development using sensor data fusion, in J. Świątek et al. (Eds), Information Systems Architecture and Technology: Proceedings of the 38th International Conference on Information Systems Architecture and Technology, ISAT 2017, Springer, Cham, pp. 189–199.
• [8] Coulson, S.G. (2018). Lanchester modelling of intelligence in combat, IMA Journal of Management Mathematics (2): 149–164.
• [9] Deitchman, S.J. (1962). A Lanchester model of guerrilla warfare, Operations Research 10(6): 818–827.
• [10] El-Douh, A.A.-R., Lu, S.F., Elkouny, A.A. and Amein, A. (2022). Hybrid cryptography with a one-time stamp to secure contact tracing for COVID-19 infection, International Journal of Applied Mathematics and Computer Science 32(1): 139–146, DOI: 10.34768/amcs-2022-0011.
• [11] Gambo, A. (2020). Mathematical modeling of dynamics behavior of terrorism and control, Caspian Journal of Mathematical Sciences 9(1): 68–85.
• [12] Hu, X., Lai, F., Chen, G., Zou, R. and Feng, Q. (2019). Quantitative research on global terrorist attacks and terrorist attack classification, Sustainability 11(5): 1487.
• [13] Janis, I.L. and Mann, L. (1977). Emergency decision making: A theoretical analysis of responses to disaster warnings, Journal of Human Stress 3(2): 35–48.
• [14] Junosza-Szaniawski, K., Nogalski, D. and Rzążewski, P. (2022). Exact and approximation algorithms for sensor placement against DDoS attacks, International Journal of Applied Mathematics and Computer Science 32(1): 35–49, DOI: 10.34768/amcs-2022-0004.
• [15] Kebir, O., Nouaouri, I., Belhadj, M. and Ben Said, L. (2020a). A multi-agent model for countering terrorism, in H. Fujita et al. (Eds), Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 19th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques (SoMeT_20), IOS Press, Amsterdam, p. 260.
• [16] Kebir, O., Nouaouri, I., Belhadj, M. and Bensaid, L. (2020b). A multi-agent model for countering terrorism, Proceedings of the 33rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE_20), Kitakyushu, Japan, pp. 1–8.
• [17] Kebir, O., Nouaouri, I., Belhaj, M., Ben Said, L. and Akrout, K. (2020c). A multi-agent architecture for modeling organizational planning against terrorist attacks in urban areas, 2020 International Multi-Conference on Organization of Knowledge and Advanced Technologies (OCTA), Tunis, Tunisia, pp. 1–8.
• [18] Kebir, O., Nouaouri, I., Belhaj, M., Said, L.B. and Akrout, K. (2020d). MAMCTA—Multi-agent model for counter terrorism actions, Revue de l’Information Scientifique et Technique 25(1): 76–90.
• [19] Kebir, O., Nouaouri, I., Rejeb, L. and Said, L.B. (2022). Simulating actors’ behaviors within terrorist attacks scenarios based on a multi-agent system, Proceedings of the 12th International Defence and Homeland Security Simulation Worskhop (DHSS 2022), Rome, Italy, pp. 12–20.
• [20] Kebir, O., Nouaouri, I., Rejeb, L. and Said, L.B. (2021). Conceptual terrorist attacks classification: Pre-processing for artificial intelligence-based classification, Proceedings of the 11th International Defence and Homeland Security Simulation Workshop (DHSS 2021), Kraków, Poland, pp. 16–24.
• [21] Kress, M., Caulkins, J.P., Feichtinger, G., Grass, D. and Seidl, A. (2018). Lanchester model for three-way combat, European Journal of Operational Research 264(1): 46–54, DOI: 10.1016/j.ejor.2017.07.026.
• [22] Kress, M. and Szechtman, R. (2009). Why defeating insurgencies is hard: The effect of intelligence in counter-insurgency operations—A best-case scenario, Operations Research 57(3): 578–585.
• [23] Lanchester, F.W. (1916). Aircraft in Warfare: The Dawn of the Fourth Arm, Constable, London.
• [24] Lee, H.-K. and Zo, H. (2017). Assimilation of military group decision support systems in Korea: The mediating role of structural appropriation, Information Development 33(1): 14–28.
• [25] Lucas, T.W. and McGunnigle, J.E. (2003). When is model complexity too much? Illustrating the benefits of simple models with Hughes’ salvo equations, Naval Research Logistics 50(3): 197–217.
• [26] Maureen, A. (2017). Military mission combat efficiency, Journal of Defense Resources Management 8(1 (14)): 63–76.
• [27] Okoye, C., Collins, O. and Mbah, G. (2020). Mathematical approach to the analysis of terrorism dynamics, Security Journal 33: 427–438, DOI: 10.1057/s41284-020-00235-5.
• [28] Oladejo, M., Udoh, I. and Abam, A. (2020). Optimizing the community’s supports in counter-terrorism operations: A sticks–carrots game theoretic model, Journal of Applied Science and Technology 39(47): 45–67.
• [29] Osoba, O.A. and Kosko, B. (2017). Fuzzy cognitive maps of public support for insurgency and terrorism, Journal of Defense Modeling and Simulation 14(1): 17–32.
• [30] Pagán, J.V. (2010). Improving the classification of terrorist attacks: A study on data pre-processing for mining the Global Terrorism Database, ICSTE 2010—2nd International Conference on Software Technology and Engineering, Puerto Rico, USA, Vol. 1, pp. 104–110.
• [31] Pechenkina, A.O. and Bennett, D.S. (2017). Violent and non-violent strategies of counterinsurgency, Journal of Artificial Societies and Social Simulation 20(4): 11.
• [32] Saltelli, A. and Annoni, P. (2010). How to avoid a perfunctory sensitivity analysis, Environmental Modelling & Software 25(12): 1508–1517.
• [33] Sandler, T. (2018). Terrorism: What Everyone Needs to Know, Oxford University Press, Oxford.
• [34] Seehuus, R.A., Rise, Ø.R., Hannay, J.E., Wold, R. and Matlary, P. (2020). Cloud-based decision support system for planning military operations, Technical report, Norwegian Military Academy, Oslo.
• [35] Sumithra, S. and Vadivel, R. (2021). An optimal innovation based adaptive estimation Kalman filter for accurate positioning in a vehicular ad-hoc network, International Journal of Applied Mathematics and Computer Science 31(1): 45–57, DOI: 10.34768/amcs-2021-0004.
• [36] Surdu, J.R. and Kittka, K. (2008). The deep green concept, Proceedings of the 2008 Spring Simulation Multiconference, Ottawa, Canada, pp. 623–631.
• [37] Udoh, I. and Oladejo, M. (2019). Optimal human resources allocation in counter-terrorism (CT) operation: A mathematical deterministic model, International Journal of Advances in Scientific Research and Engineering 5(1): 96–115.
• [38] Şuşnea, E. (2012). Decision support systems in military actions: Necessity, possibilities and constraints, Journal of Defense Resources Management 3(2): 131–140.
• [39] Vilanova, A., Telea, A., Scheuermann, G. and Möller, T. (2008). Investigative visual analysis of global terrorism, Eurographics, Crete, Greece, Vol. 27, p. 2008.
• [40] Willis, H.H., Morral, A., Kelly, T. and Medby, J. (2005). Estimating Terrorism Risk, RAND Corporation, Santa Monica.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)