Tandem Deep Learning Side-Channel Attack on FPGA Implementation of AES

Wang, Huanyu; Dubrova, Elena

doi:10.1007/s42979-021-00755-w

Cited by 13 publications

(2 citation statements)

References 36 publications

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“…Wang et al 27 suggested a Deep Learning Tandem Side Channel Attack on AES FPGA Implementation. By the use of deep learning methods, side‐channel attacks were a genuine danger to the execution of cryptographic algorithms.…”

Section: Related Workmentioning

confidence: 99%

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

Maheswari,

Krishnamurthy

2024

Trans Emerging Tel Tech

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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%.

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Section: Related Workmentioning

confidence: 99%

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

Maheswari,

Krishnamurthy

2024

Trans Emerging Tel Tech

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“…As it is possible for attackers to obtain access to physical equipment, the equipment itself must be equipped with cryptographic algorithms [1,2] in order to provide security not just against physical attacks but also against mathematical cryptanalysis. A security system [3,4] can be physically compromised by an attack known as a sidechannel attack.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optimization of Deep Learning Models on AES based Large Side Channel Attack Data

Muhammad,

Alzuabidi,

Ahmed

et al. 2024

KJCS

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Deep learning-based side-channel analysis is an efficient and suitable technique for profiling side-channel attacks. In order to obtain the better performance, it is highly necessary to analyze an in-depth training stage in which the optimization of relevant hyperparameters should be a vital process. During the training phase, hyperparameters that are connected to the architecture of the neural network are often selected; however, hyperparameters that impact the training process are to be effectively analyzed. This was represented by an optimized hyperparammeter that consists of considerable impact on attacking behaviour, which is the primary focus of our research. Our research has shown that even while the popular optimizers Adam and RMSprop are capable of delivering satisfactory outcomes, they are also tend to being overfit. Hence, it is necessary to use condensed training periods, simple profiling models, and explicit regularization in order to avoid this problem. On the other hand, the performance of optimizers of the SGD type is only satisfactory when momentum is used which results in slower convergence and less overfit. In conclusion, the research results provide a better use of Adagrad in the cases of longer training datasets or big profiling models.

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Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques

Ghimire,

Baligodugula,

Amsaad

2023

IFIP Advances in Information and Communication Technology

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Tandem Deep Learning Side-Channel Attack on FPGA Implementation of AES

Cited by 13 publications

References 36 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

Adaptive Optimization of Deep Learning Models on AES based Large Side Channel Attack Data

Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques

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