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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
DETECTOR Website: github.com/bitmapup/detector.
References
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)
Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. J. Comput. Syst. Sci. 60(3), 630–659 (2000)
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 2004
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)
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)
Feng, Z., Zhu, Y.: A survey on trajectory data mining: techniques and applications. IEEE Access 4, 2056–2067 (2016)
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)
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)
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)
Hu, W., Xie, D., Fu, Z., Zeng, W., Maybank, S.: Semantic-based surveillance video retrieval. IEEE Trans. Image Process. 16(4), 1168–1181 (2007)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
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)
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)
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)
Mao, Y., Zhong, H., Xiao, X., Li, X.: A segment-based trajectory similarity measure in the urban transportation systems. Sensors 17(3), 524 (2017)
Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)
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)
Sharif, M., Alesheikh, A., Tashayo, B.: Similarity measure of trajectories using contextual information and fuzzy approach, January 2018
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)
Toohey, K., Duckham, M.: Trajectory similarity measures. SIGSPATIAL Spec. 7(1), 43–50 (2015)
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 2002
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)
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)
Zolotarev, V.M.: One-Dimensional Stable Distributions, vol. 65. American Mathematical Society, Providence (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hoyos, I., Esposito, B., Nunez-del-Prado, M. (2019). DETECTOR: Automatic Detection System for Terrorist Attack Trajectories. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-11680-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11679-8
Online ISBN: 978-3-030-11680-4
eBook Packages: Computer ScienceComputer Science (R0)