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DETECTOR: Automatic Detection System for Terrorist Attack Trajectories

  • Isaias Hoyos
  • Bruno Esposito
  • Miguel Nunez-del-PradoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)

Abstract

To guarantee national security against terrorist attacks or organized crime, states must implement homeland security solutions based on ubiquitous systems to know in advance the number of suspects involved in an attack. This work proposes a method, which combines popular trajectory similarity metrics to estimate the number of attackers participating in a malicious act through the analysis of the trajectories described by the attacker’s cell phone connection to antennas (i.e. Call Detail Records). Therefore, measuring trajectory similarity in CDRs generates different challenges compared to those similar metrics applied over GPS and video datasets.

Keywords

Terrorist Trajectory Similarity 

References

  1. 1.
    Bashir, F.I., Khokhar, A.A., Schonfeld, D.: Object trajectory-based activity classification and recognition using hidden Markov models. IEEE Trans. Image Process. 16(7), 1912–1919 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. J. Comput. Syst. Sci. 60(3), 630–659 (2000)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Buzan, D., Sclaroff, S., Kollios, G.: Extraction and clustering of motion trajectories in video. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 521–524, August 2004Google Scholar
  4. 4.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226–231 (1996)Google Scholar
  5. 5.
    Fan, H., Yao, W.: A trajectory prediction method with sparsity data. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), pp. 1261–1265. IEEE (2017)Google Scholar
  6. 6.
    Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 4, 2056–2067 (2016)CrossRefGoogle Scholar
  7. 7.
    Ferreira, N., Klosowski, J.T., Scheidegger, C.E., Silva, C.T.: Vector field k-means: clustering trajectories by fitting multiple vector fields. In: Computer Graphics Forum, vol. 32, pp. 201–210. Wiley Online Library (2013)Google Scholar
  8. 8.
    Furtado, A., Alvares, L., Pelekis, N., Theodoridis, Y., Bogorny, V.: Unveiling movement uncertainty for robust trajectory similarity analysis. Int. J. Geogr. Inf. Sci. 32, 1–29 (2017)Google Scholar
  9. 9.
    Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Show me how you move and i will tell you who you are. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, SPRINGL 2010, pp. 34–41. ACM, New York (2010)Google Scholar
  10. 10.
    Hu, W., Xie, D., Fu, Z., Zeng, W., Maybank, S.: Semantic-based surveillance video retrieval. IEEE Trans. Image Process. 16(4), 1168–1181 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)CrossRefGoogle Scholar
  12. 12.
    Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 285–289. ACM, New York (2000)Google Scholar
  13. 13.
    Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604. ACM (2007)Google Scholar
  14. 14.
    Liu, L.X., Song, J.T., Guan, B., Wu, Z.X., He, K.J.: Tra-dbscan: a algorithm of clustering trajectories. In: Applied Mechanics and Materials, vol. 121, pp. 4875–4879, Trans Tech Publications (2012)Google Scholar
  15. 15.
    Mao, Y., Zhong, H., Xiao, X., Li, X.: A segment-based trajectory similarity measure in the urban transportation systems. Sensors 17(3), 524 (2017)CrossRefGoogle Scholar
  16. 16.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)CrossRefGoogle Scholar
  17. 17.
    Rayatidamavandi, M., Zhuang, Y., Rahnamay-Naeini, M.: A comparison of hash-based methods for trajectory clustering. In: 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence & Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 107–112. IEEE (2017)Google Scholar
  18. 18.
    Sharif, M., Alesheikh, A., Tashayo, B.: Similarity measure of trajectories using contextual information and fuzzy approach, January 2018Google Scholar
  19. 19.
    Sharif, M., Alesheikh, A.A.: Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. GIScience Remote Sens. 54(3), 426–452 (2017)CrossRefGoogle Scholar
  20. 20.
    Toohey, K., Duckham, M.: Trajectory similarity measures. SIGSPATIAL Spec. 7(1), 43–50 (2015)CrossRefGoogle Scholar
  21. 21.
    Vlachos, M., Gunopulos, D., Kollios, G.: Robust similarity measures for mobile object trajectories. In: Proceedings of 13th International Workshop on Database and Expert Systems Applications, pp. 721–726, September 2002Google Scholar
  22. 22.
    Wang, Y., Wang, L., Li, Y., He, D., Liu, T.Y., Chen, W.: A theoretical analysis of NDCG type ranking measures. CoRR abs/1304.6480 (2013)Google Scholar
  23. 23.
    Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1135–1138. IEEE (2006)Google Scholar
  24. 24.
    Zolotarev, V.M.: One-Dimensional Stable Distributions, vol. 65. American Mathematical Society, Providence (1986)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Isaias Hoyos
    • 1
  • Bruno Esposito
    • 1
  • Miguel Nunez-del-Prado
    • 1
    Email author
  1. 1.Universidad del PacíficoLimaPeru

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