Please use this identifier to cite or link to this item:
https://rda.sliit.lk/handle/123456789/2094
Title: | SEAMS: A Symmetric Encryption Algorithm Modification System to Resist Power Based Side Channel Attacks |
Authors: | Pathirana, K. P. A. P Lankarathne, L. R. M. O Hangawaththa, N. H. A. D. A Abeywardena, K. Y Kuruwitaarachchi, N |
Keywords: | Cryptography Encryption Side channel attacks Machine learning Power analysis |
Issue Date: | 2-Nov-2018 |
Publisher: | Springer, Cham |
Citation: | Pathirana, K.P.A.P., Lankarathne, L.R.M.O., Hangawaththa, N.H.A.D.A., Abeywardena, K.Y., Kuruwitaarachchi, N. (2019). SEAMS: A Symmetric Encryption Algorithm Modification System to Resist Power Based Side Channel Attacks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_70 |
Series/Report no.: | SAI 2018: Intelligent Computing Volume 857 Issue Advances in Intelligent Systems and Comp;Pages 965-976 |
Abstract: | Side channel attacks which examine physical characteristics of a cryptographic algorithm, are getting much more popular in present days since it is easier to mount an attack in a short time with only a few hundred dollars’ worth of devices. Sensitive information of a cryptographic module can be easily identified by evaluating the side channel information, such as power consumption, heat and electromagnetic emissions that outputs from the cryptographic device. This creates a huge impact on the security of the cryptographic modules as it is an efficient technique to break cryptographic algorithms by analyzing the patterns of the side channel information without having any specialized knowledge in cryptography. The solution proposed in this paper is an algorithm modification system for symmetric algorithms in order to mitigate side channel attacks. This is achieved by injecting randomness to the algorithm following a comprehensive analysis of power fluctuations that outputs from a given algorithm. In the proposed solution, a hardware device tracks down the patterns in power consumption and analyze those meter readings by utilizing machine learning techniques. As a result of this analysis, it identifies the pattern generating source code positions. System will add random code fragments in to the identified positions in the algorithm without altering the output in order to resist side channel attacks. |
URI: | http://rda.sliit.lk/handle/123456789/2094 |
ISBN: | 978-3-030-01177-2 |
Appears in Collections: | Department of Computer Systems Engineering-Scopes Research Papers - SLIIT Staff Publications |
Files in This Item:
File | Description | Size | Format | |
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Intelligent Computing Proceedings of the 2018 Computing Conference, Volume 2 (Kohei Arai, Supriya Kapoor, Rahul Bhatia) ( (1).pdf Until 2050-12-31 | 1.22 MB | Adobe PDF | View/Open Request a copy |
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