2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning

Kashyap, Priyank; Aydin, Furkan; Potluri, Seetal; Franzon, Paul D.; Aysu, Aydın

doi:10.1109/tcad.2020.3038701

Cited by 12 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As ML model, we used a convolutional neural network (CNN) architecture which is similar to the work in [19,21]. Table 2 shows the details of network structures and parameters.…”

Section: Evaluation Results With Compilermentioning

confidence: 99%

Leaking secrets in homomorphic encryption with side-channel attacks

Aydin,

Aysu

2024

J Cryptogr Eng

Self Cite

View full text Add to dashboard Cite

Homomorphic encryption (HE) allows computing encrypted data in the ciphertext domain without knowing the encryption key. It is possible, however, to break fully homomorphic encryption (FHE) algorithms by using side channels. This article demonstrates side-channel leakages of the Microsoft SEAL HE library. The proposed attack can steal encryption keys during the key generation phase by abusing the leakage of ternary value assignments that occurs during the number theoretic transform (NTT) algorithm. We propose two attacks, one for -O0 flag non-optimized code implementation which targets addition and subtraction operations, and one for -O3 flag compiler optimization which targets guard and mul root operations. In particular, the attacks can steal the secret key coefficients from a single power/electromagnetic measurement trace of SEAL's NTT implementation. To achieve high accuracy with a single-trace, we develop novel machine-learning side-channel profilers. On an ARM Cortex-M4F processor, our attacks are able to extract secret key coefficients with an accuracy of 98.3% when compiler optimization is disabled, and 98.6% when compiler optimization is enabled. We finally demonstrate that our attack can evade an application of the random delay insertion defense.

show abstract

“…As ML model, we used a convolutional neural network (CNN) architecture which is similar to the work in [19,21]. Table 2 shows the details of network structures and parameters.…”

Section: Evaluation Results With Compilermentioning

confidence: 99%

Leaking secrets in homomorphic encryption with side-channel attacks

Aydin,

Aysu

2024

J Cryptogr Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…order to get a better understanding of the efficacy of machine learning methodologies and the ways in which a change in a setting might alter the performance of the SCA. By utilizing the tools of Singular Vector Canonical Correlation Analysis (SVCCA), Van der Valk et al made the first step towards the explainability of deep neural networks in SCA [17]. This was accomplished by explaining about learning of neural networks while being trained on a variety of sidechannel information-sets.…”

Section: International Journal Of Professional Studiesmentioning

confidence: 99%

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

Fattah

2024

IJPS

View full text Add to dashboard Cite

The effectiveness of deep learning techniques has increased in recent years, making it possible to assess side channel attacks on System-on-Chips (SoCs). Specifically, this is due to the fact that they provide a sophisticated method to capitalise on the unintended loss of information that occurs during cryptographic procedures. In this particular scenario, it is very necessary to collect information on side channels, such as power consumption or electromagnetic emissions. There is a procedure called as preprocessing that involves cleaning and modifying the raw data in order to make it more acceptable for input into neural networks. The specifics of the side channel information are what define whether or not a deep learning architecture, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, or other specialist structures, should be used. Following this, the architecture of the model is painstakingly created, consisting of layers, units, and activation functions, with the purpose of effectively collecting and deciphering the intricate patterns that are associated with the processing of sensitive data on the SOC.As part of this study, we presented a CNN-dependent profiled SCA assault that was directed against an AES cypher that was operating on a SoC. During the phase of setup, we focus on the discharge from a minor capacitor that is linked with the principal line of power supply. This capacitor produces a distinct signal, and we do not capture electromagnetic emission of chip surface. When it comes to the matching and profiling phases, CNN is then used. In light of the fact that the most recent AES round did not uncover any leaks, it is recommended that a novel approach be used in order to retrieve the keys from the first round, Through the use of AES algorithm observations, we were able to develop a differential equation that included the selected intermediate value and basic text. This equation in the end resulted in possibility of advantageous for incorrect value.

show abstract

“…As a result, the attacker can only use one power measurement to implement the SCA. 9,10 Block ciphers are common cryptographic primitives which not only create data confidentiality as well as provide the base for a plethora of extra cryptographic protocols and algorithms. 11 With the rise in usage of smart IoT (Internet of Things) gadgets and other limited in resources devices, lighter block ciphers have become more and more popular in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, as PQKE protocols produce a unique secret key for every execution within the protocol, applying DPA with them is impractical. As a result, the attacker can only use one power measurement to implement the SCA 9,10 …”

Section: Introductionmentioning

confidence: 99%

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

2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning

Cited by 12 publications

References 28 publications

Leaking secrets in homomorphic encryption with side-channel attacks

Leaking secrets in homomorphic encryption with side-channel attacks

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

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

Contact Info

Product

Resources

About