DOI QR코드

DOI QR Code

Identifying the leaders and main conspirators of the attacks in terrorist networks

  • Abhay Kumar, Rai (Department of Computer Science, Banasthali Vidyapith) ;
  • Sumit, Kumar (Centre of Computer Education, IPS, University of Allahabad)
  • Received : 2021.08.13
  • Accepted : 2022.02.20
  • Published : 2022.12.10

Abstract

This study proposes a novel method for identifying the primary conspirators involved in terrorist activities. To map the information related to terrorist activities, we gathered information from different sources of real cases involving terrorist attacks. We extracted useful information from available sources and then mapped them in the form of terrorist networks, and this mapping provided us with insights in these networks. Furthermore, we came up with a novel centrality measure for identifying the primary conspirators of a terrorist attack. Because the leaders of terrorist attacks usually direct conspirators to conduct terrorist activities, we designed a novel algorithm that can identify such leaders. This algorithm can identify terrorist attack leaders even if they have less connectivity in networks. We tested the effectiveness of the proposed algorithms on four real-world datasets and conducted an experimental evaluation, and the proposed algorithms could correctly identify the primary conspirators and leaders of the attacks in the four cases. To summarize, this work may provide information support for security agencies and can be helpful during the trials of the cases related to terrorist attacks.

Keywords

References

  1. A. Berzinji, L. Kaati, and A. Rezine, Detecting key players in terrorist networks, (Proceedings of the 2012 European Intelligence and Security Informatics Conference, Odense, Denmark), Aug. 2012. https://doi.org/10.1109/EISIC.2012.13
  2. D. Bright, C. Whelan, and S. Harris-Hogan, On the durability of terrorist networks: Revealing the hidden connections between jihadist cells, Stud. Confl. Terrorism 43 (2020), no. 7, 638-656. https://doi.org/10.1080/1057610X.2018.1494411
  3. H. A. Eiselt, Destabilization of terrorist networks, Chaos Solitons Fractals 108 (2018), 111-118. https://doi.org/10.1016/j.chaos.2018.01.018
  4. I. Gialampoukidis, G. Kalpakis, T. Tsikrika, S. Papadopoulos, S. Vrochidis, and I. Kompatsiaris, Detection of terrorismrelated twitter communities using centrality scores, (Proceedings of the 2nd international workshop on multimedia forensics and security, Bucharest, Romania) 2017, pp. 21-25.
  5. I. Gialampoukidis, G. Kalpakis, T. Tsikrika, S. Vrochidis, and I. Kompatsiaris, Key player identification in terrorism-related social media networks using centrality measures, (European Intelligence and Security Informatics Conference, Uppsala, Sweden), Aug. 2016. https://doi.org/10.1109/EISIC.2016.029
  6. H. Isah, D. Neagu, and P. Trundle, Bipartite network model for inferring hidden ties in crime data, (IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France), Aug. 2015. https://doi.org/10.1145/2808797.2808842
  7. P. Mitzias, E. Kontopoulos, J. Staite, T. Day, G. Kalpakis, T. Tsikrika, H. Gibson, S. Vrochidis, B. Akhgar, and I. Kompatsiaris, Deploying semantic web technologies for information fusion of terrorism-related content and threat detection on the web, (IEEE/WIC/ACM International Conference on Web Intelligence-companion, Thessaloniki, Greece,) Oct. 2019, pp. 193-199.
  8. R. Pelzer, Policing of terrorism using data from social media, Eur. J. Secur. Res. 3 (2018), no. 2, 163-179. https://doi.org/10.1007/s41125-018-0029-9
  9. S. Singh, S. K. Verma, and A. Tiwari, A novel method for destabilization of terrorist network, Modern Phys. Lett. B 34 (2020), no. 27. https://doi.org/10.1142/S021798492050298X
  10. M. K. Sparrow, The application of network analysis to criminal intelligence: An assessment of the prospects, Soc. Netw. 13 (1991), no. 3, 251-274. https://doi.org/10.1016/0378-8733(91)90008-H
  11. S. Tutun, M. T. Khasawneh, and J. Zhuang, New framework that uses patterns and relations to understand terrorist behaviors, Expert Syst. Appl. 78 (2017), 358-375. https://doi.org/10.1016/j.eswa.2017.02.029
  12. M. Almoqbel and S. Xu, Computational mining of social media to curb terrorism, ACM Comput. Surv. 52 (2019), no. 5, 1-25. https://doi.org/10.1145/3342101
  13. V. Behzadan, A. Nourmohammadi, M. Gunes, and M. Yuksel, On fighting fire with fire: Strategic destabilization of terrorist networks, (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia), July 2017, pp. 1120-1127.
  14. M. Burcher and C. Whelan, Social network analysis as a tool for criminal intelligence: Understanding its potential from the perspectives of intelligence analysts, Trends Organized Crime. 21 (2018), no. 3, 278-294. https://doi.org/10.1007/s12117-017-9313-8
  15. M. Gregori and U. Merlone, Comparing operational terrorist networks, Trends Organized Crime 23 (2020), no. 3, 263-288. https://doi.org/10.1007/s12117-020-09381-z
  16. M. Lalou, M. A. Tahraoui, and H. Kheddouci, The critical node detection problem in networks: A survey, Comput. Sci. Rev. 28 (2018), 92-117. https://doi.org/10.1016/j.cosrev.2018.02.002
  17. F. Ozgul, Analysis of topologies and key players in terrorist networks, Socio-Econ. Plann. Sci. 56 (2016), 40-54. https://doi.org/10.1016/j.seps.2016.07.002
  18. F. Saidi, Z. Trabelsi, K. Salah, and H. B. Ghezala, Approaches to analyze cyber terrorist communities: Survey and challenges, Comput. Secur. 66 (2017), 66-80. https://doi.org/10.1016/j.cose.2016.12.017
  19. Z. Su, K. Ren, R. Zhang, and S. Y. Tan, Disrupting terrorist networks based on link prediction: A case study of the 9-11 hijackers network, IEEE Access 7 (2019), 61689-61696. https://doi.org/10.1109/access.2019.2915938
  20. J. Xu and H. Chen, The topology of dark networks, Commun. ACM 51 (2008), no. 10, 58-65.
  21. S. F. Everton and D. Cunningham, Detecting significant changes in dark networks, Behav. Sci. Terrorism Political Aggress. 5 (2013), no. 2, 94-114. https://doi.org/10.1080/19434472.2012.725225
  22. S. Wasserman and K. Faust, Social network analysis: Methods and applications, Cambridge University Press, 1994.
  23. T. Opsahl and P. Panzarasa, Clustering in weighted networks, Soc. Netw. 31 (2009), no. 2, 155-163. https://doi.org/10.1016/j.socnet.2009.02.002
  24. V. Latora and M. Marchiori, Efficient behavior of small-world networks, Phys. Rev. Lett. 87 (2001), no. 19, 198701.
  25. L. C. Freeman, Centrality in social networks conceptual clarification, Soc. Netw. 1 (1978), no. 3, 215-239. https://doi.org/10.1016/0378-8733(78)90021-7
  26. Y. Cui, X. Wang, and J. Li, Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient, Phys. A: Stat. Mech. Appl. 405 (2014), 85-91. https://doi.org/10.1016/j.physa.2014.03.027
  27. D. Li, X. Wang, and P. Huang, A fractal growth model: Exploring the connection pattern of hubs in complex networks, Phys. A: Stat. Mech. Appl. 471 (2017), 200-211. https://doi.org/10.1016/j.physa.2016.12.038
  28. G. Li, J. Hu, Y. Song, Y. Yang, and H. J. Li, Analysis of the terrorist organization alliance network based on complex network theory, IEEE Access 7 (2019), 103,854-103,862.
  29. H. J. Li, Z. Bu, Z. Wang, and J. Cao, Dynamical clustering in electronic commerce systems via optimization and leadership expansion, IEEE Trans. Ind. Informat. 16 (2019), no. 8, 5327-5334. https://doi.org/10.1109/tii.2019.2960835
  30. H. J. Li, Q. Wang, S. Liu, and J. Hu, Exploring the trust management mechanism in self-organizing complex network based on game theory, Phys. A: Stat. Mech. Appl. 542 (2020), 123514.
  31. H. J. Li, Z. Wang, J. Pei, J. Cao, and Y. Shi, Optimal estimation of low-rank factors via feature level data fusion of multiplex signal systems, IEEE Trans. Knowl. Data Eng. 34 (2020), no. 6, 2860-2871.
  32. J. Li, X. Wang, and J. Eustace, Detecting overlapping communities by seed community in weighted complex networks, Phys. A: Stat. Mech. Appl. 392 (2013), no. 23, 6125-6134. https://doi.org/10.1016/j.physa.2013.07.066
  33. F. Nian, C. Hu, S. Yao, L. Wang, and X. Wang, An immunization based on node activity, Chaos Solitons Fractals 107 (2018), 228-233. https://doi.org/10.1016/j.chaos.2018.01.013
  34. F. Nian and X. Wang, Efficient immunization strategies on complex networks, J. Theor. Biol. 264 (2010), no. 1, 77-83. https://doi.org/10.1016/j.jtbi.2010.01.007
  35. H. H. Qiao, Z. H. Deng, H. J. Li, J. Hu, Q. Song, and L. Gao, Research on historical phase division of terrorism: An analysis method by time series complex network, Neurocomputing 420 (2021), 246-265. https://doi.org/10.1016/j.neucom.2020.07.125
  36. X. Wang and J. Li, Detecting communities by the core-vertex and intimate degree in complex networks, Phys. A: Stat. Mech. Appl. 392 (2013), no. 10, 2555-2563. https://doi.org/10.1016/j.physa.2013.01.039
  37. X. Wang and T. Zhao, Model for multi-messages spreading over complex networks considering the relationship between messages, Commun. Nonlinear Sci. Numer. Simul. 48 (2017), 63-69. https://doi.org/10.1016/j.cnsns.2016.12.019
  38. X. Wang, T. Zhao, and X. Qin, Model of epidemic control based on quarantine and message delivery, Phys. A: Stat. Mech. Appl. 458 (2016), 168-178. https://doi.org/10.1016/j.physa.2016.04.009
  39. A. H. Johnston, and G. M. Weiss, Identifying sunni extremist propaganda with deep learning, (IEEE Symposium Series on Computational Intelligence, Honolulu, HI, USA), 2017. https://doi.org/10.1109/SSCI.2017.8280944
  40. M. Moussaoui, M. Zaghdoud, and J. Akaichi, A possibilistic framework for the detection of terrorism-related twitter communities in social media, Concurr. Comput: Pract. Experience 31 (2019), no. 13, e5077.
  41. J. Rasheed, U. Akram, and A. K. Malik, Terrorist network analysis and identification of main actors using machine learning techniques, (Proceedings of the 6th international conference on information technology: Iot and smart city, Hong Kong, China), 2018, pp. 7-12.
  42. M. I. Uddin, N. Zada, F. Aziz, Y. Saeed, A. Zeb, S. A. Ali-Shah, M. A. Al-Khasawneh, and M. Mahmoud, Prediction of future terrorist activities using deep neural networks, Complexity 2020 (2020), 1373087. https://doi.org/10.1155/2020/1373087
  43. P. C. Wang, and C. T. Li, Spotting terrorists by learning behavior-aware heterogeneous network embedding, (Proceedings of the 28th ACM International Conference on Information and Knowledge Management, New York, NY, USA), Nov. 2019, pp. 2097-2100.
  44. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to algorithms, MIT Press, 2009.
  45. D. B. Johnson, A note on Dijkstra's shortest path algorithm, J. ACM 20 (1973), no. 3, 385-388. https://doi.org/10.1145/321765.321768
  46. G. Sabidussi, The centrality index of a graph, Psychometrika 31 (1966), no. 4, 581-603. https://doi.org/10.1007/BF02289527
  47. A. Tundis, L. Bock, V. Stanilescu, and M. Muhlhauser, Limits in the data for detecting criminals on social media, (Proceedings of the 14th International Conference on Availability, Reliability and Security, Canterbury, UK), Aug. 2019, pp. 1-8.
  48. T. Hindu, Rajiv's death-A revisit, 2016. Available from: https://www.thehindu.com/in-school/rajivs-death-a-revisit/article5814423.ece [last accessed May 2021].
  49. The Supreme Court of India, Death Reference Case No.1 of 1998, 1999. Available from: https://web.archive.org/web/20111102222525/http://cbi.nic.in/dop/judgements/thomas.pdf [last accessed May 2021].
  50. T. Hindu, A perfect day for democracy, 2016. Available from: https://www.thehindu.com/opinion/lead/a-perfect-day-fordemocracy/article4397705.ece [last accessed May 2021].
  51. The Supreme Court of India, Appeal (criminal) 373-375 of 2004, 2005. Available from: https://main.sci.gov.in/jonew/judis/27092.pdf [last accessed May 2021].
  52. A. Tak, Afzal Guru interview with Shams Tahir Khan, 2019. Available from: https://www.youtube.com/watch?v=Mat54XtiQgA [last accessed May 2021].
  53. T. Hindu, Pak Army officers trained 26/11 terrorists, 2010. Available from: https://www.thehindu.com/news/national/lsquoPak-Army-officers-trained-2611-terroristsrsquo/article165 50267.ece [last accessed May 2021].
  54. The Supreme Court of India, Appeal (criminal) 1899-1900 of 2011, 2012. Available from: https://main.sci.gov.in/jonew/judis/39511.pdf [last accessed May 2021].
  55. I. Today, Tutor of the 26/11 terrorists, 2020. Available from: https://www.indiatoday.in/india/north/story/26-11-mumbaiattacks-pakistan-isi-let-zabiuddin-ansari-107634-2012-07-01 [last accessed May 2021].
  56. The Supreme Court of India, Criminal Appeal No. 1728 of 2007, 2013. Available from: https://main.sci.gov.in/jonew/judis/40190.pdf [last accessed December 2021].
  57. Wikipedia, 1993 Bombay bombings, 2021. Available from: https://en.wikipedia.org/wiki/1993_Bombay_bombings [last accessed December 2021].
  58. L. C. Freeman, S. P. Borgatti, and D. R. White, Centrality in valued graphs: A measure of betweenness based on network flow, Soc. Netw. 13 (1991), no. 2, 141-154.  https://doi.org/10.1016/0378-8733(91)90017-N