Smart Device Stealing and CANDIES

  • Martin JänickeEmail author
  • Viktor Schmidt
  • Bernhard Sick
  • Sven Tomforde
  • Paul Lukowicz
  • Jörn Schmeißing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11352)


Personal devices such as smart phones are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user’s needs. In this article, we focus on improving the self-awareness of such systems in terms of detecting theft: We equip devices with the capabilities to model their own user and to, e.g., alarm the legal owner if an unexpected other person is carrying the device. We gathered 24 h of data in a case study with 14 persons using a Nokia N97 and trained an activity recognition system. Using the data from this study, we investigated several autonomous novelty detection techniques, that ultimately led to the development of CANDIES. The algorithm is able to continuously check if the observed user behavior corresponds to the initial model, triggering an alarm if not. Our evaluations show that the presented methods are highly successful with a theft detection rate of over 85% for the trained set of persons. Comparing the experiments with state of the art techniques support the strong practicality of our approach.


Smart devices Gaussian mixture model Organic computing Self-awareness CANDIES Probabilistic theft detection 



The authors would like to thank the German research foundation (Deutsche Forschungsgemeinschaft, DFG) for the financial support in the context of the “Organic Computing Techniques for Runtime Self-Adaptation of Multi-Modal Activity Recognition Systems” project (SI 674/12-1, LU 1574/2-1).


  1. 1.
    Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999). Scholar
  2. 2.
    Amer, M., Goldstein, M., Abdennadher, S.: Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013, pp. 8–15. ACM, New York (2013).
  3. 3.
    Atlas, L.E., Cohn, D.A., Ladner, R.E.: Training connectionist networks with queries and selective sampling. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems 2, pp. 566–573. Morgan-Kaufmann (1990).
  4. 4.
    Bishop, C.M.: Pattern Recognition and Machine Learning. In: Bishop, C.M. (ed.) Variational Inference, pp. 461–486. Springer, New York (2006)Google Scholar
  5. 5.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  6. 6.
    Boas, M.L.: Fourier series and transforms. In: Boas, M.L. (ed.) Mathematical Methods in the Physical Sciences, pp. 375–377. Wiley, Hoboken (2006)zbMATHGoogle Scholar
  7. 7.
    Gruhl, C., Sick, B., Wacker, A., Tomforde, S., Hähner, J.: A building block for awareness in technical systems: online novelty detection and reaction with an application in intrusion detection. In: 2015 IEEE 7th International Conference on Awareness Science and Technology (iCAST), pp. 194–200 (2015)Google Scholar
  8. 8.
    Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012). Scholar
  9. 9.
    Clarkson, B., Mase, K., Pentland, A.: Recognizing user context via wearable sensors. In: Proceedings of ISWC, pp. 69–75 (2000)Google Scholar
  10. 10.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
  11. 11.
    Fernández-Francos, D., Martínez-Rego, D., Fontenla-Romero, O., Alonso-Betanzos, A.: Automatic bearing fault diagnosis based on one-class \(\nu \)-SVM. Comput. Ind. Eng. 64(1), 357–365 (2013). Scholar
  12. 12.
    Fisch, D., Jänicke, M., Kalkowski, E., Sick, B.: Learning by teaching versus learning by doing: knowledge exchange in organic agent systems. In: IEEE Symposium on Intelligent Agents, IA 2009, pp. 31–38. IEEE (2009)Google Scholar
  13. 13.
    Fisch, D., Jänicke, M., Kalkowski, E., Sick, B.: Learning from others: exchange of classification rules in intelligent distributed systems. Artif. Intell. 187–188, 90–114 (2012). Scholar
  14. 14.
    Franke, T., Lukowicz, P., Kunze, K., Bannach, D.: Can a mobile phone in a pocket reliably recognize ambient sounds? In: International Symposium on Wearable Computers, ISWC 2009, pp. 161–162. IEEE (2009)Google Scholar
  15. 15.
    Gellersen, H., Schmidt, A., Beigl, M.: Multi-sensor context-awareness in mobile devices and smart artifacts. Mob. Netw. Appl. 7(5), 341–351 (2002)CrossRefGoogle Scholar
  16. 16.
    Gruhl, C., Sick, B.: Detecting novel processes with CANDIES - an holistic novelty detection technique based on probabilistic models. CoRRabs/1605.05628 (2016).
  17. 17.
    Guerbai, Y., Chibani, Y., Hadjadji, B.: The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recogn. 48(1), 103–113 (2015). Scholar
  18. 18.
    Jänicke, M., Schmidt, V., Sick, B., Tomforde, S., Lukowicz, P.: Hijacked smart devices - methodical foundations for autonomous theft awareness based on activity recognition and novelty detection. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, pp. 131–142. INSTICC, SciTePress (2018).
  19. 19.
    Junker, H., Amft, O., Lukowicz, P., Tröster, G.: Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recogn. 41(6), 2010–2024 (2008). Scholar
  20. 20.
    Kephart, J., Chess, D.: The vision of autonomic computing. IEEE Comput. 36(1), 41–50 (2003)CrossRefGoogle Scholar
  21. 21.
    Kwapisz, J., Weiss, G., Moore, S.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newsl. 12(2), 74–82 (2011)CrossRefGoogle Scholar
  22. 22.
    Lau, S., David, K.: Movement recognition using the accelerometer in smartphones. In: Future Network and Mobile Summit, pp. 1–9. IEEE (2010)Google Scholar
  23. 23.
    Lau, S., König, I., David, K., Parandian, B., Carius-Düssel, C., Schultz, M.: Supporting patient monitoring using activity recognition with a smartphone. In: 2010 7th International Symposium on Wireless Communication Systems (ISWCS), pp. 810–814. IEEE (2010)Google Scholar
  24. 24.
    Li, K.L., Huang, H.K., Tian, S.F., Xu, W.: Improving one-class SVM for anomaly detection. In: 2003 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3077–3081. IEEE (2003)Google Scholar
  25. 25.
    Markou, M., Singh, S.: Novelty detection: a review - part 1: statistical approaches. Sig. Process. 83, 2481–2497 (2003)CrossRefGoogle Scholar
  26. 26.
    Markou, M., Singh, S.: Novelty detection: a review - part 2: neural network based approaches. Sig. Process. 83, 2499–2521 (2003)CrossRefGoogle Scholar
  27. 27.
    Mitra, S., et al.: An affordable, long-lasting, and autonomous theft detection and tracking system. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 2009, pp. 351–352. ACM, New York (2009).
  28. 28.
    Müller-Schloer, C., Tomforde, S.: Organic Computing - Technical Systems for Survival in the Real World. Autonomic Systems. Verlag, Birkhäuser (2017). ISBN 978-3-319-68476-5CrossRefGoogle Scholar
  29. 29.
    Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C., et al.: Support vector method for novelty detection. In: NIPS, vol. 12, pp. 582–588 (1999)Google Scholar
  30. 30.
    Sun, L., Zhang, D., Li, B., Guo, B., Li, S.: Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 548–562. Springer, Heidelberg (2010). Scholar
  31. 31.
    Tax, D., Duin, R.: Uniform object generation for optimizing one-class classifiers. J. Mach. Learn. Res. 2, 155–173 (2002)zbMATHGoogle Scholar
  32. 32.
    Tomforde, S., Sick, B., Müller-Schloer, C.: Organic Computing in the Spotlight, arXiv, January 2017.
  33. 33.
    Zhang, Y., Meratnia, N., Havinga, P.: Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks. In: 2009 International Conference on Advanced Information Networking and Applications Workshops, pp. 990–995, May 2009.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Martin Jänicke
    • 1
    Email author
  • Viktor Schmidt
    • 1
  • Bernhard Sick
    • 1
  • Sven Tomforde
    • 1
  • Paul Lukowicz
    • 2
  • Jörn Schmeißing
    • 3
  1. 1.Intelligent Embedded SystemsUniversity of KasselKasselGermany
  2. 2.German Research Center for Artificial IntelligenceKaiserslauternGermany
  3. 3.University of KasselKasselGermany

Personalised recommendations