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



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