Abstract
Anomaly detection of the robot system behavior is one of the important components of the information security control. In order to control robots equipped with many sensors it is difficult to apply the well-known Mahalanobis distance which allows us to analyze the current state of the sensors. Therefore, the Siamese neural network is proposed to intellectually support the security control. The Siamese network simplifies the anomaly detection of the robot system and realizes a non-linear analogue of the Mahalanobis distance. This peculiarity allows us to take into account complex data structures received from the robot sensors.
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References
Bartkowiak, A.M., Anomaly, novelty, one-class classification: A comprehensive introduction, Int. J. Comput. Inf. Syst. Ind. Manage. Appl., 2011, vol. 3, pp. 61–71.
Bayindir, L., A review of swarm robotics tasks, Neurocomputing, 2016, vol. 172, pp. 292–321.
Brereton, R.G., The Mahalanobis distance and its relationship to principal component scores, J. Chemom., 2015, vol. 29, pp. 143–145.
Bromley, J., Bentz, J.W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Sackinger, E., and Shah, R., Signature verification using a Siamese time delay neural network, Int. J. Pattern Recognit. Artif. Intell., 1993, vol. 7, no. 4, pp. 737–744.
Chandola, V., Banerjee, A., and Kumar, V., Anomaly detection: A survey, ACM Comput. Surv., 2009, vol. 41, no. 3, pp. 1–58.
Chopra, S., Hadsell, R., and LeCun, Y., Learning a similarity metric discriminatevely, with application to face verification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, vol. 1, pp. 539–546.
Erfani, S.M., Rajasegarar, S., Karunasekera, S., and Leckie, C., High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning, Pattern Recognit., 2016, vol. 58, pp. 121–134.
Hu, J., Lu, J., and Tan, Y.-P., Discriminative deep metric learning for face verification in the wild, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1875–1882.
Khalastchi, E., Kalech, M., Kaminka, G.A., and Lin, R., Online data-driven anomaly detection in autonomous robots, Knowl. Inf. Syst., 2015, vol. 43, no. 3, pp. 657–688.
Khalastchi, E., Kalech, M., and Rokach, L., Sensor fault detection and diagnosis for autonomous systems, Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), 2013, pp. 15–22.
Koch, G., Zemel, R., and Salakhutdinov, R., Siamese neural networks for one-shot image recognition, Proceedings of the 32nd International Conference on Machine Learning, Lille, 2015, vol. 37, pp. 1–8.
Li, C., Georgiopoulos, M., and Anagnostopoulos, G.C., Kernel-based distance metric learning in the output space, The 2013 International Joint Conference on Neural Networks (IJCNN), 2013, pp. 1–8.
Mendoza, J.P., Veloso, M., and Simmons, R., Detecting and correcting model anomalies in subspaces of robot planning domains, Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), Istanbul, 2015, pp. 1587–1595.
Navarro, I. and Matia, F., An introduction to swarm robotics, ISRN Rob., 2013, vol. ID 608164, pp. 1–10.
Pan, S. and Yang, Q., A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 2010, vol. 22, no. 10, pp. 1345–1359.
Pennisi, A., Previtali, F., Ficarola, F., Bloisi, D.D., Iocchi, L., and Vitaletti, A., Distributed sensor network for multi-robot surveillance, Procedia Comput. Sci., 2014, vol. 32, pp. 1095–1100.
Pimentel, M.A.F., Clifton, D.A., Clifton, L., and Tarassenko, L., A review of novelty detection, Signal Process., 2014, vol. 99, pp. 215–249.
Pronobis, A., Mozos, O.M., and Caputo, B., SVM-based discriminative accumulation scheme for place recognition, Proceedings of the IEEE International Conference on Robotics and Automation, 2008, pp. 522–529.
Saldana, D., Assunceao, R., and Campos, M.F.M., A distributed multi-robot approach for the detection and tracking of multiple dynamic anomalies, IEEE International Conference on Robotics and Automation (ICRA), 2015, pp. 1262–1267.
Shalev-Shwartz, S., Singer, Y., and Ng, A.Y., Online and batch learning of pseudo-metrics, Proceedings of the 21st International Conference on Machine Learning, Banff, 2004, pp. 94–101.
Tan, Y. and Zheng, Z.-Y., Research advance in swarm robotics, Def. Technol., 2013, vol. 9, pp. 18–39.
Thomaz, C.E., Gillies, D.F., and Feitosa, R.Q., A new covariance estimate for Bayesian classifiers in biometric recognition, IEEE Trans. Circuits Syst. Video Technol., 2004, vol. 14, no. 2, pp. 214–223.
Wang, B., Wang, L., Shuai, B., Zuo, Z., Liu, T., Luk, C.K., and Wang, G., Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016, pp. 1–8.
Zielinski, C. and Kornuta, T., Diagnostic requirements in multi-robot systems, in Intelligent Systems in Technical and Medical Diagnostics, Berlin: Springer, 2014, pp. 345–356.
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Original Russian Text © L.V. Utkin, V.S. Zaborovsky, S.G. Popov, 2017, published in Problemy Informatsionnoi Bezopasnosti, Komp’yuternye Sistemy.
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Utkin, L.V., Zaborovsky, V.S. & Popov, S.G. Siamese neural network for intelligent information security control in multi-robot systems. Aut. Control Comp. Sci. 51, 881–887 (2017). https://doi.org/10.3103/S0146411617080235
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DOI: https://doi.org/10.3103/S0146411617080235