Deep Learning Side-Channel Attack Against Hardware Implementations of AES

2019 ◽  
Author(s):  
Takaya Kubota ◽  
Kota Yoshida ◽  
Mitsuru Shiozaki ◽  
Takeshi Fujino
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Attack ◽  
Hardware Implementations

Deep learning side-channel attack against hardware implementations of AES

2020 ◽  
pp. 103383
Author(s):  
Takaya Kubota ◽  
Kota Yoshida ◽  
Mitsuru Shiozaki ◽  
Takeshi Fujino
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Attack ◽  
Hardware Implementations

scholarly journals Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 22480-22492
Author(s):  
Yoo-Seung Won ◽  
Dong-Guk Han ◽  
Dirmanto Jap ◽  
Shivam Bhasin ◽  
Jong-Yeon Park
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Attack

Power Side-Channel Analysis of RNS GLV ECC Using Machine and Deep Learning Algorithms

2021 ◽  
Vol 21 (3) ◽  
pp. 1-20
Author(s):  
Mohamad Ali Mehrabi ◽  
Naila Mukhtar ◽  
Alireza Jolfaei
Keyword(s):  
Deep Learning ◽  
Elliptic Curve ◽  
Smart Cities ◽  
Information Leakage ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Public Key Cryptosystems ◽  
Elliptic Curve Cryptosystems ◽  
Hardware Implementations ◽  
Substantial Progress

Many Internet of Things applications in smart cities use elliptic-curve cryptosystems due to their efficiency compared to other well-known public-key cryptosystems such as RSA. One of the important components of an elliptic-curve-based cryptosystem is the elliptic-curve point multiplication which has been shown to be vulnerable to various types of side-channel attacks. Recently, substantial progress has been made in applying deep learning to side-channel attacks. Conceptually, the idea is to monitor a core while it is running encryption for information leakage of a certain kind, for example, power consumption. The knowledge of the underlying encryption algorithm can be used to train a model to recognise the key used for encryption. The model is then applied to traces gathered from the crypto core in order to recover the encryption key. In this article, we propose an RNS GLV elliptic curve cryptography core which is immune to machine learning and deep learning based side-channel attacks. The experimental analysis confirms the proposed crypto core does not leak any information about the private key and therefore it is suitable for hardware implementations.

Acoustic Side Channel Attack for Device Identification using Deep Learning Models

2021 ◽  
Author(s):  
V S Adhin ◽  
Arunjo Maliekkal ◽  
K Mukilan ◽  
K Sanjay ◽  
R Chitra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Learning Models ◽  
Side Channel Attack ◽  
Device Identification

Multi-label Deep Learning based Side Channel Attack

2020 ◽  
pp. 1-1
Author(s):  
Libang Zhang ◽  
Xinpeng Xing ◽  
Junfeng Fan ◽  
Zongyue Wang ◽  
Suying Wang
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Attack
Sign in / Sign up

Export Citation Format

Share Document