Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1076468).
References
- Dodge, Samuel, and Lina Karam. "A study and comparison of human and deep learning recognition performance under visual distortions." 2017 26th international conference on computer communication and networks (ICCCN). IEEE, 2017.
- Gohr, Aron. "Improving attacks on round-reduced speck32/64 using deep learning." Advances in Cryptology-CRYPTO 2019: 39th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 18-22, 2019, Proceedings, Part II 39. Springer International Publishing, 2019.
- E. Tcydenova, "Cryptanalysis of Lightweight Block Ciphers Based on Neural Distinguisher," MS Thesis, Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea, 2021.
- E. Tcydenova, B. Seok, and C. Lee, "Related-key Neural Distinguisher on Lightweight Block Ciphers SPECK-32/64, HIGHT, SIMECK-32/64 and CHAM-64/128", KIISC 2021, Yeongnam Branch, Korea Institute of Information Security and Cryptology, 2021.
- Ko, Youngdai, et al. "Related key differential attacks on 27 rounds of XTEA and full-round GOST." Fast Software Encryption: 11th International Workshop, FSE 2004, Delhi, India, February 5-7, 2004. Revised Papers 11. Springer Berlin Heidelberg, 2004.
- He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Baksi, Anubhab, and Anubhab Baksi. "Machine learning-assisted differential distinguishers for lightweight ciphers." Classical and Physical Security of Symmetric Key Cryptographic Algorithms (2022): 141-162.
- So, Jaewoo. "Deep learning-based cryptanalysis of lightweight block ciphers." Security and Communication Networks 2020 (2020): 1-11.
- Yadav, Tarun, and Manoj Kumar. "Differential-ml distinguisher: Machine learning based generic extension for differential cryptanalysis." Progress in Cryptology-LATINCRYPT 2021: 7th International Conference on Cryptology and Information Security in Latin America, Bogota, Colombia, October 6-8, 2021, Proceedings. Cham: Springer International Publishing, 2021.
- Bellini, Emanuele, and Matteo Rossi. "Performance comparison between deep learning-based and conventional cryptographic distinguishers." Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume 3. Springer International Publishing, 2021.
- Benamira, Adrien, et al. "A deeper look at machine learning-based cryptanalysis." Advances in Cryptology-EUROCRYPT 2021: 40th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Zagreb, Croatia, October 17-21, 2021, Proceedings, Part I 40. Springer International Publishing, 2021.
- Watanabe, Dai, Alex Biryukov, and Christophe De Canniere. "A distinguishing attack of SNOW 2.0 with linear masking method." Selected Areas in Cryptography: 10th Annual International Workshop, SAC 2003, Ottawa, Canada, August 14-15, 2003. Revised Papers 10. Springer Berlin Heidelberg, 2004.
- Biham, Eli, Orr Dunkelman, and Nathan Keller. "Related-Key Boomerang and Rectangle Attacks." Eurocrypt. Vol. 3494. 2005.
- Hong, Deukjo, et al. "HIGHT: A new block cipher suitable for low-resource device." Cryptographic Hardware and Embedded Systems-CHES 2006: 8th International Workshop, Yokohama, Japan, October 10-13, 2006. Proceedings 8. Springer Berlin Heidelberg, 2006.
- Poschmann, Axel, San Ling, and Huaxiong Wang. "256 bit standardized crypto for 650 GE-GOST revisited." Cryptographic Hardware and Embedded Systems, CHES 2010: 12th International Workshop, Santa Barbara, USA, August 17-20, 2010. Proceedings 12. Springer Berlin Heidelberg, 2010.
- Popescu, Marius-Constantin, et al. "Multilayer perceptron and neural networks." WSEAS Transactions on Circuits and Systems 8.7 (2009): 579-588.
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Communications of the ACM 60.6 (2017): 84-90. https://doi.org/10.1145/3065386
- Xie, Saining, et al. "Aggregated residual transformations for deep neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.