The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Breaking Cryptographic Implementations Using Deep Learning Techniques
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Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
An Introduction to Genetic Algorithms
Mitchell, M. (1996) · doi:10.7551/mitpress/3927.001.0001 · Cited by 11,340
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
Hochreiter, S. (1998) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488598000094 · Cited by 2,939
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
Restricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392
Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning
Gohr, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-26951-7_6 · Cited by 207
A Deeper Look at Machine Learning-Based Cryptanalysis
Benamira, A., Gerault, D., Peyrin, T., & Tan, Q. Q. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-77870-5_28 · Cited by 130
The Threat of Offensive AI to Organizations
Mirsky, Y., Demontis, A., Kotak, J., Shankar, R., Gelei, D., Yang, L., Zhang, X., Pintor, M., Lee, W., Elovici, Y., et al. (2023) · Computers & Security · doi:10.1016/j.cose.2022.103006 · Cited by 97
Metadata
- Authors
- Maghrebi, H., Portigliatti, T., & Prouff, E.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Volume
- 2016
- Pages
- 3-26
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2556867355
- Cited by
- 541
- References
- 9
- FWCI
- 14.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Breaking Cryptographic Implementations Using Deep Learning Techniques. https://app.cochat.ai/papers/10.1007/978-3-319-49445-6_1
- The paper
- Maghrebi, H., Portigliatti, T., & Prouff, E. (2016). Breaking Cryptographic Implementations Using Deep Learning Techniques. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-49445-6_1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
