Attacking AES implementations using correlation power analysis on ZYBO Zynq-7000 SoC board

Socha, Petr; Brejnik, Jan; Bartík, Matěj

doi:10.1109/meco.2018.8406034

Cited by 7 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input as well as the output of the encoding layer c are designated by the letters A c and B c , respectively, along with the letters A C and B C for the corresponding decoding layer C. When back-propagating losses while training, the residual link between matching layers reduces the data loss. Equation (5) shows the link between the pertinent quantities:…”

Section: Deep Residual Capsule Autoencodermentioning

confidence: 99%

“…Multiple traces on the same device are gathered with different sub-keys (a component within a cryptographic key) throughout the profiling stage and the model is created. [4][5][6] Lattice-based cryptographic works are efficiently used in both software and hardware environments. Using power-based SCAs, hackers try to figure out the hidden key through the relationship among the cryptographic device's battery usage and the computations that utilize the secret key.…”

Section: Introductionmentioning

confidence: 99%

“…Profiled strength assault and profiling are the two phases of a SCA attack. Multiple traces on the same device are gathered with different sub‐keys (a component within a cryptographic key) throughout the profiling stage and the model is created 4–6 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

Maheswari,

Krishnamurthy

2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Side‐channel analysis (SCA) is a type of cryptanalytic attack that uses unintended ‘side‐channel’ leakage through the real‐world execution of the cryptographic algorithm to crack a secret key of an embedded system. These side‐channel errors can be discovered through tracking the energy usage of the device performing the technique, electromagnetic radiations while the encryption process, execution time, cache hits/misses, and others. Nowadays, deep learning‐based detection techniques are considered as emerging techniques that have been proposed for attack detection. Deep learning architectures have the ability to learn autonomously and concentrate on difficult features, in contrast to machine learning models. In light of these factors, the work's motive is thought to be the proposal of a deep learning‐based attack detection method. Many methods are used to decrease these assaults, however, the majority of them are inefficient and time‐demanding. In order to address these challenges, this study employs a novel deep learning‐based methodology. Pre‐processing, feature extraction, and SCA classification are the three stages of the approach proposed in this work. First, pre‐processing is used to remove unnecessary information and improve the quality of the input using data cleaning and min‐max normalization. The previously processed data are then fed as input into the proposed hybrid deep learning architecture. A Deep Residual Capsule Auto‐Encoder (DR_CAE) model is introduced in the proposed study. The deep residual neural network‐50 (DRNN‐50) is utilized to extract relevant features in this case, while the side channel analysis is done by using capsule auto‐encoder (CAE). The parameters of the proposed model are adjusted using the modified white shark optimization (MWSO) technique to improve its performance. In the results section, the proposed model is compared to various existing models in terms of accuracy, precision, recall, F‐measures, time, and so on. The proposed framework has an accuracy of 98.802%, F‐measures of 98.801%, kappa coefficient of 97.6%, the precision value of 98.81%, and recall value of 98.80%.

show abstract

Section: Deep Residual Capsule Autoencodermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

Maheswari,

Krishnamurthy

2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Cryptography is used to secure any form of communication system [10]- [12] by scrambling the information so that only the intended recipient can access it. Cryptography uses two general schemes: hashing and encryption.…”

Section: Introductionmentioning

confidence: 99%

Modified AES Cipher Round and Key Schedule

Reyes¹,

Sison²,

Medina³

2019

IJEEI

View full text Add to dashboard Cite

In this paper, Advanced Encryption Standard was modified to address the low diffusion rate at the early rounds by adding additional primitive operations such as exclusive OR and modulo arithmetic in the cipher round. Furthermore, byte substitution and round constant addition were appended to the key schedule algorithm. The modified AES was tested against the standard AES by means of avalanche effect and frequency test to measure the diffusion and confusion characteristics respectively. The results of the avalanche effect evaluation show that there was an average increase in diffusion of 61.98% in round 1, 14.79% in round 2 and 13.87% in round 3. Consequently, the results of the frequency test demonstrated an improvement in the randomness of the ciphertext since the average difference between the number of ones to zeros is reduced from 11.6 to 6.4 along with better-computed p-values. The results clearly show that the modified AES has improved diffusion and confusion properties and the ciphertext can still be successfully decrypted and recover back the original plaintext.

show abstract

Power Side Channel Attack of AES FPGA Implementation with Experimental Results using Full Keys

Mozipo

Acken

2021

2021 IEEE International Conference on Design &Amp; Test of Integrated Micro &Amp; Nano-Systems (DTS)

View full text Add to dashboard Cite

Attacking AES implementations using correlation power analysis on ZYBO Zynq-7000 SoC board

Cited by 7 publications

References 10 publications

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

Modified AES Cipher Round and Key Schedule

Power Side Channel Attack of AES FPGA Implementation with Experimental Results using Full Keys

Contact Info

Product

Resources

About