Abstract
Security in embedded systems is considered to be more important and needs to be a diagnosis for every minute. Also with the advent of the Internet of Things (IoT), security in the embedded system has reached its new peak of dimension. A Mathematically secure algorithm was formulated and runs on the cryptographic chips which are embedded in the systems, but secret keys can be at risk and even information can be retrieved by the prominent side-channel attacks. Fixed encryption keys, non-intelligent detection of side-channel attacks are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on the integration of powerful machine learning algorithms by retrieving the secret key information with countermeasures methodology using the chaotic logistic maps and includes the following contributions: (a) Preparation of Data Sets from the Power consumption traces captured from ARTIX-7 FPGA boards while running the Elliptical Curve Cryptography(ECC) on it (b) Implementation of High Speed and High Accurate Single feed-forward learning machines for the detection and classification of side-channel attacks (c) Design of Chaotic Countermeasures using 3-Dlogistic maps for attacked bits. The test_bed has been developed using the integration of FPGA along with Cortex-A57 architectures for experimentation of the proposed work and various evaluation parameters such as Accuracy, F-calls, Precision rates, sensitivity, and correlation co-efficient, entropy were calculated and analyzed. Moreover, the parameters of the proposed system which has been analyzed prove to outperform the other existing algorithms in terms of performance and detection.
Similar content being viewed by others
Change history
29 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04236-7
References
Bhasin S, Danger J, Guilley S, Najm Z (2015) Side-channel leakage and trace compression using normalized inter-class variance. In: Proceedings of the 3rd international workshop on hardware and architectural support for security and privacy, HASP, Portland, OR, USA, 14 June 2015, p 7
Blake I, Seroussi G, Seroussi G, Smart N (1999) Elliptic curves in cryptography. Cambridge University Press, Cambridge
Das D, Golder A, Danial J, Ghosh S, Raychowdhury A, Sen S (2019) X-DeepSCA: Cross-device deep learning side channel attack. In: proceedings of the 56th ACM/IEEE design automation conference (DAC)
Genkin D, Shamir A, Tromer E (2014) RSA key extraction via low-bandwidth acoustic cryptanalysis. In: Proceedings of the advances in cryptology—CRYPTO 2014: 34th annual cryptology conference, Santa Barbara, CA, USA, 17–21 August 2014, pp 444–461
Gilmore R, Hanley N, O'Neill M (2015) Neural network-based attack on a masked implementation of AES. In: Proceedings of the hardware oriented security and trust (HOST), Washington, DC, 5–7 May 2015, pp 106–111
Hospodar G, Mulder ED, Gierlichs B, Verbauwhede I, Vandewalle J (2011) Least squares support vector machines for side-channel analysis. In: Proceedings of the 2nd workshop on constructive side-channel analysis and secure design (COSADE), Darmstadt, Germany, 24–25 February 2011
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501
Javed AR, Beg MO, Asim M et al (2020) Alpha logger: detecting motion-based side-channel attack using smartphone keystrokes. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01770-0
Kadir SA, Sasongko A, Zulkifli M (2011) Simple power analysis attack against elliptic curve cryptography processor on FPGA implementation. In: Proceedings of the 2011 international conference on electrical engineering and informatics, Bandung, Indonesia, 17–19 July 2011, pp 1–4
Kocher PC, Jaffe J, Jun B (1999) Differential power analysis. In: Proceedings of the advances in cryptology—CRYPTO’ 99: 19th annual international cryptology conference, Santa Barbara, CA, USA, 15–19 August 1999; Springer, Berlin/Heidelberg, pp 388–397
Kocher PC (1996) Timing attacks on implementations of Diffie–Hellman, RSA, DSS, and other systems. In: proceedings of the advances in cryptology—CRYPTO ’96: 16th annual international cryptology conference, Santa Barbara, 18–22 August 1996; Springer, Berlin/Heidelberg, pp 104–113
Lerman L, Bontempi G, Markowitch O (2013) A machine learning approach against a masked AES. J Cryptogr Eng 5:123–139
Liu D, Zhang C, Lin H, Chen Y, Zhang M (2018) A resource-efficient and side-channel secure hardware implementation of ring-lwe cryptographic processor. IEEE Trans Circ Syst I Reg Pap 66(4):1474–83
Longo J, DeMulder E, Page D, Tunstall M (2015) SoCittoEM: electromagnetic side-channel attacks on a complex System-on-chip; cryptographic hardware and embedded systems—CHES; lecture notes in computer science, vol 9293. Springer, Berlin, pp 620–640
Lu S, Lu Z, Yang J, Yang M, Wang S (2016) A pathological brain detection system based on kernel based ELM. Multimed Tools Appl 77(3):3715–28
Mukhtar N (2018) Mohamad ali mehrabi, yinan kong and ashiq anjum, “machine-learning-based side-channel evaluation of elliptic-curve cryptographic fpga processor”. Appl Sci 9:64. https://doi.org/10.3390/app9010064
Ors SB, Oswald E, Preneel B (2003) Power-analysis attacks on an FPGA—first experimental results. In: proceedings of the cryptographic hardware and embedded systems (CHES), Cologne, 8–10 September 2003. Springer, Berlin/Heidelberg, pp 35–50
Rivest RL (1991) Cryptography and machine-learning. In: proceedings of the advances in cryptology—ASIACRYPT ’91: international conference on the theory and application of cryptology, Fuji Yoshida, Japan, 11–14 November 1991; Springer, Berlin/Heidelberg, pp 427–439
Saeedi E, Kong Y, Hossain MS (2017) Side-channel attacks and learning-vector quantization. Front Inform Technol Electron Eng 18(4):511–8
Shan W, Zhang S, He Y (2017) Machine learning based side-channel-attack countermeasure with hamming-distance redistribution and its application on advanced encryption standard. Electron Lett 53(14):926–8
Singh A, Chawla N, Ko J-H (2019) Energy efficient and side-channel secure cryptographic hardware for IoT-edge Nodes. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2018.2861324
Souissi Y, Nassar M, Guilley S, Danger JL, Flament F (2010) First principal components analysis: a new side-channel distinguisher. Proc Int Conf Inf Secur Cryptol Seoul Korea 1–3:407–419
Srivastava A, Ghosh P (2019) An efficient memory zeroization technique under side-channel attacks. In: IEEE-32nd international conference on VLSI design and 2019 18th international conference on embedded systems (VLSID), pp 76–81. https://doi.org/10.1109/VLSID.2019.00032
Standaert FX, Tot Oldenzeel LVO, Samyde D, Quisquater JJ (2003) Power analysis of FPGAs: how practical is the attack? In: Cheung P YK, Constantinides GA (eds) Proceedings of the field programmable logic and application, Lisbon, Portugal, 1–3 September 2003; Springer, Berlin/Heidelberg, Germany, pp 701–710
Wang B, Huang S, Qiu J et al (2015) Parallel online sequential extreme learning machine based on MapReduce. Neurocomputing 149:224–232
Zhao M, Edward Suh G (2018) FPGA-based remote power side-channel attacks. In: 2018 IEEE symposium on security and privacy
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04236-7
About this article
Cite this article
Illuri, B., Jose, D. RETRACTED ARTICLE: Design and implementation of hybrid integration of cognitive learning and chaotic countermeasures for side channel attacks. J Ambient Intell Human Comput 12, 5427–5441 (2021). https://doi.org/10.1007/s12652-020-02030-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02030-x