Abstract
Security is an essential part of development in embedded systems. Execution of unknown or malicious program through an unauthorized means of communication on an embedded system can cause unwanted system behavior. To safeguard the sensitive data and devices, presently, sophisticated hardware and software systems based on cryptographic techniques are required which in turn increases the system’s cost. In this paper, we proposed a method of securing such embedded devices which cannot afford to have capabilities comparable to conventional computers. This method generates a run-time trace on embedded devices during program execution, using already available hardware circuitry on the board. It observes and analyzes the obtained data using data analysis techniques and detects whether any change is occurred in the program compared to previously obtained data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boufounos, P. and Rane, S.: November. secure binary embeddings for privacy preserving nearest neighbors. In: 2011 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2011)
Hopkins, A.B., McDonald-Maier, K.D.: Debug support strategy for systems-on-chips with multiple processor cores. IEEE Trans. Comput. 55(2), 174–184 (2006)
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(1), 3–32 (2011)
Maier, K.D.: On-chip debug support for embedded systems-on-chip. In: Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS’03. vol. 5, pp. V–V. IEEE (2003)
Collberg, C., Carter, E., Debray, S., Huntwork, A., Kececioglu, J., Linn, C., Stepp, M.: Dynamic path-based software watermarking. ACM Sigplan Not. 39(6), 107–118 (2004)
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 (2008)
Panagakis, Y., Kotropoulos, C.: December. Telephone handset identification by feature selection and sparse representations. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 73–78. IEEE (2012)
Swaminathan, A., Mao, Y., Wu, M., Kailas, K.: Data hiding in compiled program binaries for enhancing computer system performance. In: Barni, M., Herrera-JoancomartÃ, J., Katzenbeisser, S., Pérez-González F. (eds.) Information Hiding, vol. 3727. Springer, Berlin (2006)
Kohonen, T.: Learning vector quantization. In: Michael, A.A. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 537–540. MIT Press (1998)
Arora, D., Ravi, S., Raghunathan, A., Jha, N.K.: Secure embedded processing through hardware-assisted run-time monitoring. In: Proceedings of the Conference on Design, Automation and Test in Europe-Volume 1, pp. 178–183. IEEE Computer Society (2005)
Handschuh, H., Schrijen, G.J., Tuyls, P.: Hardware intrinsic security from physically unclonable functions. In: Towards Hardware-Intrinsic Security, pp. 39–53. Springer, Heidelberg (2010)
Kolbitsch, C., Comparetti, P.M., Kruegel, C., Kirda, E., Zhou, X.Y., Wang, X.: Effective and efficient malware detection at the end host. In: USENIX Security Symposium, pp. 351–366 (2009)
Studnia, I., Nicomette, V., Alata, E., Deswarte, Y., Kaâniche, M., Laarouchi, Y.: Survey on security threats and protection mechanisms in embedded automotive networks. In: 2013 43rd Annual IEEE Dependable Systems and Networks Workshop (DSN-W) (2013)
Costin, A., Zaddach, J., Francillon, A., Balzarotti, D., Antipolis, S.: A large-scale analysis of the security of embedded firmwares. In: USENIX Security Symposium, pp. 95–110 (2014)
Tran, M., Etheridge, M., Bletsch, T., Jiang, X., Freeh, V., Ning, P.: On the expressiveness of return-into-libc attacks. In: Recent Advances in Intrusion Detection, pp. 121–141. Springer, Heidelberg (2011)
Ravi, S., Raghunathan, A., Chakradhar, S.: Tamper resistance mechanisms for secure embedded systems. In: 2004 Proceedings. 17th International Conference on VLSI Design, pp. 605–611. IEEE (2004)
Zhai, X., Appiah, K., Ehsan, S., Howells, G., Hu, H., Gu, D., McDonald-Maier, K.D.: A method for detecting abnormal program behavior on embedded devices. IEEE Trans. Inf. Forensics Secur. 10(8), 1692–1704 (2015)
Eyerman, S., Eeckhout, L., Karkhanis, T., Smith, J.E.: A performance counter architecture for computing accurate CPI components. ACM SIGOPS Oper. Syst. Rev. 40(5), 175–184 (2006). ACM
Nowlan, S.J.: Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures (1991)
Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21(3), 193–199 (2002)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singhal, G., Roy, S. (2019). A Novel Method to Detect Program Malfunctioning on Embedded Devices Using Run-Time Trace. In: Rawat, B., Trivedi, A., Manhas, S., Karwal, V. (eds) Advances in Signal Processing and Communication . Lecture Notes in Electrical Engineering, vol 526. Springer, Singapore. https://doi.org/10.1007/978-981-13-2553-3_47
Download citation
DOI: https://doi.org/10.1007/978-981-13-2553-3_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2552-6
Online ISBN: 978-981-13-2553-3
eBook Packages: EngineeringEngineering (R0)