More insight on deep learning-aided cryptanalysis
In CRYPTO 2019, Gohr showed that well-trained neural networks could perform
cryptanalytic distinguishing tasks superior to differential distribution table (DDT)-based …
cryptanalytic distinguishing tasks superior to differential distribution table (DDT)-based …
An assessment of differential-neural distinguishers
Since the introduction of differential-neural cryptanalysis, as the machine learning assisted
differential cryptanalysis proposed in [Goh19] is coined by now, a lot of followup works have …
differential cryptanalysis proposed in [Goh19] is coined by now, a lot of followup works have …
Improved neural distinguishers with multi-round and multi-splicing construction
JS Liu, JJ Ren, SZ Chen, MM Li - Journal of Information Security and …, 2023 - Elsevier
In CRYPTO 2019, Gohr successfully applied deep learning to differential cryptanalysis
against the NSA block cipher Speck32/64, achieving higher accuracy than traditional …
against the NSA block cipher Speck32/64, achieving higher accuracy than traditional …
Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck
X Yue, W Wu - Applied Sciences, 2023 - mdpi.com
At CRYPTO 2019, Gohr proposed the neural differential distinguisher using the residual
network structure in convolutional neural networks on round-reduced Speck32/64. In this …
network structure in convolutional neural networks on round-reduced Speck32/64. In this …
[PDF][PDF] Machine learning-based lightweight block ciphers for resource-constrained internet of things networks: a review.
The increasing number of internet of things (IoT) devices, wearable technologies, and
embedded systems has experienced a significant increase in recent years. This surge has …
embedded systems has experienced a significant increase in recent years. This surge has …
Improved differential-neural cryptanalysis for round-reduced simeck32/64
L Zhang, J Lu, Z Wang, C Li - Frontiers of Computer Science, 2023 - Springer
Conclusion In this study, we have developed a neural network aimed at enhancing the
precision of neural distinguishers, demonstrating its capability to surpass DDT-based …
precision of neural distinguishers, demonstrating its capability to surpass DDT-based …
A deep learning aided differential distinguisher improvement framework with more lightweight and universality
JS Liu, JJ Ren, SZ Chen - Cybersecurity, 2023 - Springer
In CRYPTO 2019, Gohr opens up a new direction for cryptanalysis. He successfully applied
deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64 …
deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64 …
Theoretical Explanation and Improvement of Deep Learning-aided Cryptanalysis
W Zheng, L Zhang, Z Wang - Cryptology ePrint Archive, 2024 - eprint.iacr.org
At CRYPTO 2019, Gohr demonstrated that differential-neural distinguishers (DNDs) for
Speck32/64 can learn more features than classical cryptanalysis's differential distribution …
Speck32/64 can learn more features than classical cryptanalysis's differential distribution …
Improving the Accuracy of Differential-Neural Distinguisher for DES, Chaskey, and PRESENT
In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for S
PECK 32/64. A differential-neural distinguisher was obtained using ResNet neural network …
PECK 32/64. A differential-neural distinguisher was obtained using ResNet neural network …
بررسی جامع رویکردهای یادگیری عمیق در تحلیل تفاضلی رمزهای قالبی سبکوزن
میرزاعلی مازندرانی, ایمان, باقری, نصور… - دوفصل نامه علمی منادی …, 2023 - monadi.isc.org.ir
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