scholarly journals Design of Testability Structures With Security For Machine Intelligence Based Cryptosystem

Author(s):  
G Sowmiya ◽  
S. Malarvizhi

Abstract During testing utmost all appropriate and suitable strategy needs to be established for consistent fault coverage, improved controllability and observability. The scan chains used in BIST allows some fine control over data propagations that is used as a backdoor to break the security over cryptographic cores. To alleviate these scan-based side-channel attacks, implementing a more inclusive security strategy is required to confuse the attacker and to ensure the key management process which is always a difficult task to task in cryptographic research. In this work for testing AES core Design-for-Testability (DfT) is considered with some random response compaction, bit masking during the scan process. In the proposed scan architecture, scan-based attack does not allow finding out actual computations which are related to the cipher transformations and key sequence. And observing the data through the scan structure is secured. The experimental results validate the potential metrics of the proposed scan model in terms of robustness to the scan attack and penalty gap that exists due to the inclusion of scan designs in AES core. Also investigate the selection of appropriate location points to implement the bit level modification to avoid attack for retrieving a key.

2009 ◽  
Vol 19 (11) ◽  
pp. 2990-2998 ◽  
Author(s):  
Tao ZHANG ◽  
Ming-Yu FAN

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.


2021 ◽  
Vol 21 (3) ◽  
pp. 1-20
Author(s):  
Mohamad Ali Mehrabi ◽  
Naila Mukhtar ◽  
Alireza Jolfaei

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.


2021 ◽  
pp. 1-1
Author(s):  
Youshui Lu ◽  
Yong Qi ◽  
Saiyu Qi ◽  
Fuyou Zhang ◽  
Wei Wei ◽  
...  

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