scholarly journals EFFICIENT CNN-BASED PROFILED SIDE CHANNEL ATTACKS

2021 ◽  
Vol 37 (1) ◽  
pp. 1-22
Author(s):  
Ngoc Quy Tran ◽  
Hong Quang Nguyen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Side Channel ◽  
Grey Wolf ◽  
Side Channel Attacks ◽  
Grey Wolf Optimization ◽  
Points Of Interest ◽  
Public Dataset ◽  
The Right ◽  
Dpa Contest

Profiled side-channel attacks are now considered as powerful forms of attacks used to break the security of cryptographic devices. A recent line of research has investigated a new profiled attack based on deep learning and many of them have used convolution neural network (CNN) as deep learning architecture for the attack. The effectiveness of the attack is greatly influenced by the CNN architecture. However, the CNN architecture used for current profiled attacks have often been based on image recognition fields, and choosing the right CNN architectures and parameters for adaption to profiled attacks is still challenging. In this paper, we propose an efficient profiled attack for on unprotected and masking-protected cryptographic devices based on two CNN architectures, called CNNn, CNNd respectively. Both of CNN architecture parameters proposed in this paper are based on the property of points of interest on the power trace and further determined by the Grey Wolf Optimization (GWO) algorithm. To verify the proposed attacks, experiments were performed on a trace set collected from an Atmega8515 smart card when it performs AES-128 encryption, a DPA contest v4 dataset and the ASCAD public dataset

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.

scholarly journals Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

2021 ◽  
Vol 1821 (1) ◽  
pp. 012038
Author(s):  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hopfield Neural Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization

A novel hybrid grey wolf optimization algorithm and artificial neural network technique for stochastic unit commitment problem with uncertainty of wind power

2021 ◽  
pp. 014233122110019
Author(s):  
C. Venkatesh Kumar ◽  
M. Ramesh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Unit Commitment ◽  
Operating Cost ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Stochastic Unit Commitment ◽  
Hybrid Methodology

The unit commitment (UC) is highly complex to solve the increasing integrations of wind farm due to intermittent wind power fluctuation in nature. This paper presents a hybrid methodology to solve the stochastic unit commitment (SUC) problem depending on binary mixed integer generator combination with renewable energy sources (RESs). In this combination, ON/OFF tasks of the generators are likewise included to satisfy the load requirement as for the system constraints. The proposed hybrid methodology is the consolidation of grey wolf optimization algorithm (GWOA) and artificial neural network (ANN), hence it is called the hybrid GWOANN (HGWOANN) technique. Here, the GWOA algorithm is used to optimizing the best combination of thermal generators depending on uncertain wind power, minimum operating cost and system constraints – that is, thermal generators limits, start-up cost, ramp-up time, ramp-down time, etc. ANN is utilized to capture the uncertain wind power events, therefore the system ensures maximal application of wind power. The combination of HGWOANN technique guarantees the prominent use of sustainable power sources to diminish the thermal generators unit operating cost. The proposed technique is implemented in MATLAB/Simulink site and the efficiency is assessed with different existing methods. The comparative analysis demonstrates that the proposed HGWOANN approach is proficient to solve unit commitment problems and wind integration. Here, the HGWOANN method is compared with existing techniques such as PSO, BPSO, IGSA to assess the overall performance using various metrics viz. RMSE, MAPE, MBE under 50 and 100 count of trials. In the proposed approach, the range of RMSE achieves 9.26%, MAPE achieves 0.95%, MBE achieves 1% in 50 count of trials. Moreover, in 100 count of trials, the range of RMSE achieves 7.38%, MAPE achieves 1.91%, MBE achieves 2.87%.

scholarly journals Study of Dementia Severity Prognosis in Mr Images Using Grey Wolf Optimization Based Dual Deep Learning Technique

2021 ◽  
Author(s):  
Ahana priynaka ◽  
Kavitha Ganesan
Keyword(s):  
Deep Learning ◽  
Symmetry Analysis ◽  
Support Vector ◽  
Grey Wolf ◽  
Mr Images ◽  
Grey Wolf Optimization ◽  
Feature Vectors ◽  
Dementia Severity ◽  
Improved Performance ◽  
Multiclass Svm

Abstract Prognosis of in a dementia disorder is a tedious task in preclinical stage. Ventricle pathology changes in dementia appear to be overlapped for neuro degeneration in brain. Identification of these overlaps among the groups severity helps to understand the pathogenesis of this disorder. In this work impact of changes in ventricle region on severity stages of dementia is observed using dual deep learning techniques (DDLT). Alzheimer's Disease Neuroimaging Initiative (ADNI) database that contains 1169 MR images are used in this study. Segmentation of ventricle region is carried out using multilevel threshold based Grey Wolf Optimization (GWO) technique. The feature vectors obtained from combined AlexNet and ResNet are analysed. The fused feature vectors are given to support vector machine (SVM) to observe the severity changes. Consequently, symmetry analysis of ventricle is carried out to perceive the distinctive changes in progression. The obtained results show that ventricle region is accurately delineated from other region with optimized thresholds. The segmented ventricle shows better correlation for all considered classes (> 0.9). It is observed that DDLT with multiclass SVM provides an improved accuracy of about 79.87% compared to individual transfer learning such as AlexNet (74%) and ResNet (76.53%). Further, symmetry analysis shows that left side ventricle with DDLT features shows an improved performance than right side for onset stages. Further, clinical correlation of left ventricle seems to be statically significant (p<0.0001) which prominently differentiate dementia severity variations. This framework is more prominent and clinically useful to identify the distinct ventricle region variation in dementia.

scholarly journals Recognizing Ancient South Indian Language Using Artificial Neural Network Associate With Opposition Based Grey Wolf Optimization Algorithm

2021 ◽  
Author(s):  
A Nareshkumar ◽  
G Geetha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Algorithm ◽  
Test Case ◽  
Grey Wolf ◽  
Indian Languages ◽  
Grey Wolf Optimization ◽  
Indian Language ◽  
South Indian ◽  
Artificial Neural

Abstract Recognizing signs and fonts of prehistoric language is a fairly difficult job that require special tools. This stipulation makes the dispensation period overriding, difficult, and tiresome to calculate. This paper presents a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is apparent from the ANN system that arbitrarily produced weights or neurons linking various layers plays a significant role in its performance. For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization, Particle Swarm Optimization and Grey Wolf Optimization to the ANN system. Performance results have illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques. In test case 1, the accuracy value of OGWO is 94.89% and in test case 2, the accuracy value of OGWO is 92.34%, on average, the accuracy of OGWO achieves 5.8% greater accuracy than ANN-GWO, 10.1% greater accuracy than ANN-PSO and 22.1% greater accuracy over conventional ANN technique.

scholarly journals Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis

2019 ◽  
pp. 107-131 ◽  
Author(s):  
Benjamin Timon
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Deep Learning ◽  
New Method ◽  
Invariance Property ◽  
Side Channel ◽  
Translation Invariance ◽  
Secret Key ◽  
Side Channel Attacks ◽  
Learning Techniques

Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis [MPP16]. Until now, studies have been focused on applying Deep Learning techniques to perform Profiled Side-Channel attacks where an attacker has a full control of a profiling device and is able to collect a large amount of traces for different key values in order to characterize the device leakage prior to the attack. In this paper we introduce a new method to apply Deep Learning techniques in a Non-Profiled context, where an attacker can only collect a limited number of side-channel traces for a fixed unknown key value from a closed device. We show that by combining key guesses with observations of Deep Learning metrics, it is possible to recover information about the secret key. The main interest of this method is that it is possible to use the power of Deep Learning and Neural Networks in a Non-Profiled scenario. We show that it is possible to exploit the translation-invariance property of Convolutional Neural Networks [CDP17] against de-synchronized traces also during Non-Profiled side-channel attacks. In this case, we show that this method can outperform classic Non-Profiled attacks such as Correlation Power Analysis. We also highlight that it is possible to break masked implementations in black-box, without leakages combination pre-preprocessing and with no assumptions nor knowledge about the masking implementation. To carry the attack, we introduce metrics based on Sensitivity Analysis that can reveal both the secret key value as well as points of interest, such as leakages and masks locations in the traces. The results of our experiments demonstrate the interests of this new method and show that this attack can be performed in practice.

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