Open Access
Research Article
Dependency of lightweight block ciphers over S-boxes: A deep learning based analysis
*Girish MishraCorresponding authorgmishratech28@gmail.comAffiliation 1Scientific Analysis Group, Defence R & D Organization, New Delhi 110054IndiaAffiliation 2Pune 411025, MaharashtraDefence Institute of Advanced Technology (DU)IndiaView full profile → , S. V. S. S. N. V. G. Krishna Murthysgkmurthy@gmail.comPune 411025, MaharashtraDefence Institute of Advanced Technology (DU)IndiaView full profile → , S. K. PalScientific Analysis Group, Defence R & D Organization, New Delhi 110054IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Oct 2020
- Accepted:
- 28 Feb 2021
- Published Online:
- 15 Nov 2021
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-1338
- Pages:
- 153–173
Abstract
Lightweight ciphers have been proposed from time to time to tackle various usage issues in IoT (Internet of Things) scenario. PRESENT and DoT are two such ciphers. PRESENT has been a benchmark cipher to establish and validate the security features of any new lightweight block cipher. This paper presents deep learning based analysis of a widely known lightweight cipher PRESENT and relatively less known lightweight cipher DoT. Our observations show that both the ciphers are extremely sensitive to any intentional or unintentional error occurring in their S-boxes. In our experiments, these two ciphers maintain the desired immunity to such errors when either of the randomly generated ten non-linear S-boxes are used in their corresponding algorithms. However, the ciphers fail to withstand our deep learning based attack when either no S-box is used or a reverse S-box is used in their algorithms. This work validates the fact that S-box should always be carefully chosen while designing a block cipher. The observations from our work suggest the designers to be observant towards vulnerabilities arising out of any intentional fault attacks from the adversary.
Keywords
Subject Classifications
94A6068P25
References
- Andrey Bogdanov, Lars R Knudsen, Gregor Leander, Christof Paar, Axel Poschmann, Matthew JB Robshaw, Yannick Seurin, and Charlotte Vikkelsoe. Present: An ultra-lightweight block cipher. In International workshop on cryptographic hardware and embedded systems, pages 450–466. Springer, 2007. [Google Scholar]
- Jagdish Patil, Gaurav Bansod, and Kumar Shashi Kant. Dot: A new ultra-lightweight sp network encryption design for resource- constrained environment. In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology, pages 249–257. Springer, 2019. [Crossref], [Google Scholar]
- Li Deng, Dong Yu, et al. Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3–4):197–387, 2014. doi: https://doi.org/10.1561/2000000039 [Crossref], [Google Scholar]
- Martin Popel, Marketa Tomkova, Jakub Tomek, Lukasz Kaiser, Jakob Uszkoreit, Ondřej Bojar, and Zdeněk Žabokrtsky’. Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals. Nature communications, 11(1):1–15, 2020. doi: https://doi.org/10.1038/s41467-020-18073-9 [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
- Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao. Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE International Conference on Computer Vision, pages 2722–2730, 2015. [Google Scholar]
- Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Thippa Reddy Gadekallu, Chiranji Lal Chowdhary, Mamoun Alazab, Md Jalil Piran, et al. Deep learning and medical image processing for coronavirus (covid-19) pandemic: A survey. Sustainable cities and society, 65:102589, 2021. doi: https://doi.org/10.1016/j.scs.2020.102589 [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
- Arun Kumar Dubey and Vanita Jain. A review of face recognition methods using deep learning network. Journal of Information and Optimization Sciences, 40(2):547–558, 2019. doi: https://doi.org/10.1080/02522667.2019.1582875 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
- Vedika Gupta, Stuti Juyal, Gurvinder Pal Singh, Chirag Killa, and Nishant Gupta. Emotion recognition of audio/speech data using deep learning approaches. Journal of Information and Optimization Sciences, 41(6):1309–1317, 2020. doi: https://doi.org/10.1080/02522667.2020.1809089 [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
- Aron Gohr. Improving attacks on round-reduced speck32/64 using deep learning. In Annual International Cryptology Conference, pages 150–179. Springer, 2019. [Crossref], [Google Scholar]
- Anubhab Baksi, Jakub Breier, Xiaoyang Dong, and Chen Yi. Machine learning assisted differential distinguishers for lightweight ciphers. IACR Cryptol. ePrint Arch., 2020:571, 2020. [Google Scholar]
- Ronald L Rivest. Cryptography and machine learning. In International Conference on the Theory and Application of Cryptology, pages 427–439. Springer, 1991. [Crossref], [Google Scholar]
- Martín Abadi and David G Andersen. Learning to protect communications with adversarial neural cryptography. arXiv preprint arXiv:1610.06918, 2016. [Google Scholar]
- Ehsan Hesamifard, Hassan Takabi, and Mehdi Ghasemi. Cryptodl: Deep neural networks over encrypted data. arXiv preprint arXiv:1711.05189, 2017. [Google Scholar]
- Stjepan Picek, Ioannis Petros Samiotis, Jaehu
Views: 194Downloads: 68Citations: 2
