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Detecting Compromised Programs for Embedded System Applications

  • Conference paper
Architecture of Computing Systems – ARCS 2014 (ARCS 2014)

Abstract

This paper proposes an approach for detecting compromised programs by analysing suitable features from an embedded system. Features used in this paper are the performance variance and actual program counter values of the embedded processor extracted during program execution. “Cycles per Instruction” is used as pre-processing block before the features are classified using a Self-Organizing Map. Experimental results demonstrate the validity of the proposed approach on detecting some common changes such as deletion, insertion and substitution of programs. Overall, correct detection rate for our system is above 90.9% for tested programs.

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References

  1. Arora, D., Ravi, S., Raghunathan, A., Jha, N.K.: Secure embedded processing through hardware-assisted run-time monitoring. In: Proceedings Design, Automation and Test in Europe, pp. 178–183 (2005)

    Google Scholar 

  2. F-Secure Corporation: F-Secure reports amount of malware grew by 100% during 2007, Helsinki, Finland (2007)

    Google Scholar 

  3. Dongara, P., Vijaykumar, T.N.: Accelerating private-key cryptography via multithreading on symmetric multiprocessors. In: IEEE International Symposium on Performance Analysis of Systems and Software, pp. 58–69 (2003)

    Google Scholar 

  4. Rahmatian, M., Kooti, H., Harris, I.G., Bozorgzadeh, E.: Hardware-Assisted Detection of Malicious Software in Embedded Systems. IEEE Embedded Systems Letters 4, 94–97 (2012)

    Article  Google Scholar 

  5. Suh, G.E., Devadas, S.: Physical Unclonable Functions for Device Authentication and Secret Key Generation. In: 44th ACM/IEEE Design Automation Conference, pp. 9–14 (2007)

    Google Scholar 

  6. Handschuh, H., Schrijen, G.-J., Tuyls, P.: Hardware Intrinsic Security from Physically Unclonable Functions. In: Sadeghi, A.-R., Naccache, D. (eds.) Towards Hardware-Intrinsic Security, pp. 39–53. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Hospodar, G., Maes, R., Verbauwhede, I.: Machine learning attacks on 65nm Arbiter PUFs: Accurate modeling poses strict bounds on usability. In: IEEE International Workshop on Information Forensics and Security (WIFS), pp. 37–42 (2012)

    Google Scholar 

  8. Arora, D., Ravi, S., Raghunathan, A., Jha, N.K.: Secure embedded processing through hardware-assisted run-time monitoring. In: Proceedings of Design, Automation and Test in Europe, vol. 171, pp. 178–183 (2005)

    Google Scholar 

  9. Hanilci, C., Ertas, F., Ertas, T., Eskidere, O.: Recognition of Brand and Models of Cell-Phones From Recorded Speech Signals. IEEE Transactions on Information Forensics and Security 7, 625–634 (2012)

    Article  Google Scholar 

  10. Kovalchuk, Y., McDonald-Maier, K.D., Howells, G.: Overview of ICmetrics technology-security infrastructure for autonomous and intelligent healthcare system. International Journal of u- and e- Sevice, Science and Technology 4, 49–60 (2011)

    Google Scholar 

  11. Howells, G., Papoutsis, E., Hopkins, A., McDonald-Maier, K.: Normalizing Discrete Circuit Features with Statistically Independent values for incorporation within a highly Secure Encryption System. In: Second NASA/ESA Conference on Adaptive Hardware and Systems, pp. 97–102 (2007)

    Google Scholar 

  12. Kohonen, T.: Learning vector quantization. In: Michael, A.A. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 537–540. MIT Press (1998)

    Google Scholar 

  13. Deng, M., Wuyts, K., Scandariato, R., Preneel, B., Joosen, W.: A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements. Requirements Eng. 16, 3–32 (2011)

    Article  Google Scholar 

  14. Yang, R., Qu, Z., Huang, J.: Detecting digital audio forgeries by checking frame offsets. In: Proceedings of the 10th ACM Workshop on Multimedia and Security, pp. 21–26. ACM, Oxford (2008)

    Chapter  Google Scholar 

  15. Swaminathan, A., Mao, Y., Wu, M., Kailas, K.: Data Hiding in Compiled Program Binaries for Enhancing Computer System Performance. In: Barni, M., Herrera-Joancomartí, J., Katzenbeisser, S., Pérez-González, F. (eds.) IH 2005. LNCS, vol. 3727, pp. 357–371. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Boufounos, P., Rane, S.: Secure binary embeddings for privacy preserving nearest neighbors. In: IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2011)

    Google Scholar 

  17. Annavaram, M., Rakvic, R., Polito, M., Bouguet, J., Hankins, R., Davies, B.: The fuzzy correlation between code and performance predictability. In: The 37th International Symposium on Microarchitecture (MICRO), pp. 93–104 (2004)

    Google Scholar 

  18. STMicroelectronics. STM32F207G DATA Sheet, http://www.st.com/ (accessed on January 2013)

  19. KEIL. Keil uVision IDE Data Sheet, http://www.keil.com/uvision/ (accessed on January 2013)

  20. Guthaus, M.R., Ringenberg, J.S., Ernst, D., Austin, T.M., Mudge, T., Brown, R.B.: MiBench: A free, commercially representative embedded benchmark suite. In: IEEE International Workshop on Workload Characterization, pp. 3–14 (2001)

    Google Scholar 

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Zhai, X. et al. (2014). Detecting Compromised Programs for Embedded System Applications. In: Maehle, E., Römer, K., Karl, W., Tovar, E. (eds) Architecture of Computing Systems – ARCS 2014. ARCS 2014. Lecture Notes in Computer Science, vol 8350. Springer, Cham. https://doi.org/10.1007/978-3-319-04891-8_19

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  • DOI: https://doi.org/10.1007/978-3-319-04891-8_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04890-1

  • Online ISBN: 978-3-319-04891-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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