Breaking Cryptographic Implementations Using Deep Learning Techniques
By Houssem Maghrebi, Thibault Portigliatti, Emmanuel Prouff. SPACE, 2016
564 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/82024003820d52ef344fb210d050f469f60ddf4b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ankit Thakkar
- Ritika Lohiya
- Hamed Haddadi
- Paul Patras
- Chaoyun Zhang
- Mohammed Ali Al-Garadi
- A. Al-Ali
- Amr M. Mohamed
In Computer Vision and Pattern Recognition
- Ankit Thakkar
- Ritika Lohiya
- Hamed Haddadi
- Paul Patras
- Chaoyun Zhang
- Mohammed Ali Al-Garadi
- A. Al-Ali
- Amr M. Mohamed
In Hardware and Architecture
- Ankit Thakkar
- Ritika Lohiya
- Paul Patras
- Hamed Haddadi
- Chaoyun Zhang
- Mohsen Guizani
- Emmanuel Prouff
- Lejla Batina
Citations in the corpus
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “MLP, AE, CNN, LSTM) to construct a precise profiling system and perform side channel key recovery attacks [349].”
- CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel (2019, background): “Lately, several works explored the power of deep learning in the context of sidechannel analysis [32].”
- A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “A previous study [208] showed that a CNN algorithm can break cryptographic implementations successfully.”
- A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges (2020, methodology): “Moreover, in [141], CNN is used for android malware detection to provide security in IoT devices.”
- Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures - Profiling Attacks Without Pre-processing (2017, background): “Nevertheless, the fact that CNNs usually applies non-linear functions to the data makes them potentially (and practically, as already experienced in [21]) able to deal with such a countermeasure as well.”
- Deep learning for side-channel analysis and introduction to ASCAD database (2019, background): “Following the current trend in the Machine Learning area, more recent works have started to pay attention to deep learning (DL) algorithms like multi-layer perceptron networks (MLP) [46–48] or convolutional neural networks (CNN) [13,44].”