scholarly journals Side-channel Attack Using Word Embedding and Long Short Term Memories

2022 ◽  
Author(s):  
Zixin Liu ◽  
Zhibo Wang ◽  
Mingxing Ling
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Word Embedding ◽  
Side Channel ◽  
Side Channel Attack ◽  
Short Term ◽  
Proposed Model ◽  
Field Programmable ◽  
Processing Signal ◽  
Programmable Gate Arrays

Side-channel attack (SCA) based on machine learning has proved to be a valid technique in cybersecurity, especially subjecting to the symmetric-key crypto implementations in serial operation. At the same time, parallel-encryption computing based on Field Programmable Gate Arrays (FPGAs) grows into a new influencer, but the attack results using machine learning are exiguous. Research on the traditional SCA has been mostly restricted to pre-processing: Signal Noisy Ratio (SNR) and Principal Component Analysis (PCA), etc. In this work, firstly, we propose to replace Points of Interests (POIs) and dimensionality reduction by utilizing word embedding, which converts power traces into sensitive vectors. Secondly, we combined sensitive vectors with Long Short Term Memories (LSTM) to execute SCA based on FPGA crypto-implementations. In addition, compared with traditional Template Attack (TA), Multiple Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). The result shows that the proposed model can not only reduce the manual operation, such as parametric assumptions and dimensionality setting, which limits their range of application, but improve the effectiveness of side-channel attacks as well.

scholarly journals An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2272
Author(s):  
Safa Bouguezzi ◽  
Hana Ben Fredj ◽  
Tarek Belabed ◽  
Carlos Valderrama ◽  
Hassene Faiedh ◽  
...  
Keyword(s):  
Recognition Rate ◽  
Hardware Acceleration ◽  
Implementation Model ◽  
Gate Arrays ◽  
Proposed Model ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Computer Vision Applications ◽  
On Chip ◽  
Segmentation Image

Convolutional Neural Networks (CNN) continue to dominate research in the area of hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its effectiveness in a variety of computer vision applications such as object segmentation, image classification, face detection, and traffic signs recognition, among others. However, there are numerous constraints for deploying CNNs on FPGA, including limited on-chip memory, CNN size, and configuration parameters. This paper introduces Ad-MobileNet, an advanced CNN model inspired by the baseline MobileNet model. The proposed model uses an Ad-depth engine, which is an improved version of the depth-wise separable convolution unit. Moreover, we propose an FPGA-based implementation model that supports the Mish, TanhExp, and ReLU activation functions. The experimental results using the CIFAR-10 dataset show that our Ad-MobileNet has a classification accuracy of 88.76% while requiring little computational hardware resources. Compared to state-of-the-art methods, our proposed method has a fairly high recognition rate while using fewer computational hardware resources. Indeed, the proposed model helps to reduce hardware resources by more than 41% compared to that of the baseline model.

scholarly journals Side-Channel Power Resistance for Encryption Algorithms Using Implementation Diversity

Cryptography ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 13
Author(s):  
Ivan Bow ◽  
Nahome Bete ◽  
Fareena Saqib ◽  
Wenjie Che ◽  
Chintan Patel ◽  
...  
Keyword(s):  
Power Analysis ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Dynamic Partial Reconfiguration ◽  
Channel Power ◽  
Diversity Techniques ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Encryption Algorithms

This paper investigates countermeasures to side-channel attacks. A dynamic partial reconfiguration (DPR) method is proposed for field programmable gate arrays (FPGAs)s to make techniques such as differential power analysis (DPA) and correlation power analysis (CPA) difficult and ineffective. We call the technique side-channel power resistance for encryption algorithms using DPR, or SPREAD. SPREAD is designed to reduce cryptographic key related signal correlations in power supply transients by changing components of the hardware implementation on-the-fly using DPR. Replicated primitives within the advanced encryption standard (AES) algorithm, in particular, the substitution-box (SBOX)s, are synthesized to multiple and distinct gate-level implementations. The different implementations change the delay characteristics of the SBOXs, reducing correlations in the power traces, which, in turn, increases the difficulty of side-channel attacks. The effectiveness of the proposed countermeasures depends greatly on this principle; therefore, the focus of this paper is on the evaluation of implementation diversity techniques.

scholarly journals Design and Implementation of Perceptron Neuron in Machine Learning for Handwritten Character Recognition

2020 ◽  
Vol 9 (10) ◽  
pp. 357-363
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Field Programmable Gate Arrays ◽  
Fpga Implementation ◽  
Floating Point ◽  
Neuron Network ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays

Due to the exponential increase of electronic devices that are connected to the Internet, the amount of data that they produce have grown to the same extent. In order to face the processing of these data, the use of some automatic learning algorithms, also known as Machine Learning, has become widespread. The most popular is the one known as neural networks. These algorithms need a great deal of resources to compute all their operations, and because of that, they have been traditionally implemented in application specific integrated circuits. However, recently there have been a boom in implementations in field programmable gate arrays, also known as FPGAs. These allow greater parallelism in the implementation of the algorithms. Field Programmable Gate Arrays (FPGA) implementation based feature extraction method is proposed in this paper. This particular application is handwritten offline digit recognition. The classification depends on simple 2 layer MultiLayer Perceptron (MLP). The particular feature extraction approach is suitable for execution of FPGA because it is utilized with subtraction and addition operations. From Standard database handwritten digit images of normalized 40×40 pixel the features are extracted by the proposed method. It has been discovered by experiential outcomes that 85% accuracy is achieved by proposed system. Overall, as compared to other systems, it is less complex, more accurate and simple. Further this project explains IEE-754 format single precision floating point MAC unit’s FPGA implementation which is utilized for feeding the neurons weighted inputs in artificial neural networks. Data representation range is improved by floating point numbers utilization to a higher number from smaller number that is highly suggested for Artificial Neuron Network. The code is developed in HDL, simulated and synthesis results are extracted using Xilinx synthesis tools .In order to validate its computational accuracy of the FFT, an MATLAB validation script is used to verify the output of HDL with standard reference model.

scholarly journals Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1487 ◽  
Author(s):  
Musaed Alhussein ◽  
Syed Irtaza Haider ◽  
Khursheed Aurangzeb
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Wind Speed ◽  
Solar Irradiance ◽  
Energy Resources ◽  
Level Energy ◽  
Short Term ◽  
Proposed Model ◽  
Smart Community ◽  
Different Climates

Background: The Distributed Energy Resources (DERs) are beneficial in reducing the electricity bills of the end customers in a smart community by enabling them to generate electricity for their own use. In the past, various studies have shown that owing to a lack of awareness and connectivity, end customers cannot fully exploit the benefits of DERs. However, with the tremendous progress in communication technologies, the Internet of Things (IoT), Big Data (BD), machine learning, and deep learning, the potential benefits of DERs can be fully achieved, although a significant issue in forecasting the generated renewable energy is the intermittent nature of these energy resources. The machine learning and deep learning models can be trained using BD gathered over a long period of time to solve this problem. The trained models can be used to predict the generated energy through green energy resources by accurately forecasting the wind speed and solar irradiance. Methods: We propose an efficient approach for microgrid-level energy management in a smart community based on the integration of DERs and the forecasting wind speed and solar irradiance using a deep learning model. A smart community that consists of several smart homes and a microgrid is considered. In addition to the possibility of obtaining energy from the main grid, the microgrid is equipped with DERs in the form of wind turbines and photovoltaic (PV) cells. In this work, we consider several machine learning models as well as persistence and smart persistence models for forecasting of the short-term wind speed and solar irradiance. We then choose the best model as a baseline and compare its performance with our proposed multiheaded convolutional neural network model. Results: Using the data of San Francisco, New York, and Los Vegas from the National Solar Radiation Database (NSRDB) of the National Renewable Energy Laboratory (NREL) as a case study, the results show that our proposed model performed significantly better than the baseline model in forecasting the wind speed and solar irradiance. The results show that for the wind speed prediction, we obtained 44.94%, 46.12%, and 2.25% error reductions in root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), respectively. In the case of solar irradiance prediction, we obtained 7.68%, 54.29%, and 0.14% error reductions in RMSE, mean bias error (MBE), and sMAPE, respectively. We evaluate the effectiveness of the proposed model on different time horizons and different climates. The results indicate that for wind speed forecast, different climates do not have a significant impact on the performance of the proposed model. However, for solar irradiance forecast, we obtained different error reductions for different climates. This discrepancy is certainly due to the cloud formation processes, which are very different for different sites with different climates. Moreover, a detailed analysis of the generation estimation and electricity bill reduction indicates that the proposed framework will help the smart community to achieve an annual reduction of up to 38% in electricity bills by integrating DERs into the microgrid. Conclusions: The simulation results indicate that our proposed framework is appropriate for approximating the energy generated through DERs and for reducing the electricity bills of a smart community. The proposed framework is not only suitable for different time horizons (up to 4 h ahead) but for different climates.

scholarly journals 28nm asynchronous area-saving AES processor with high Common and Machine Learning Side-Channel Attack resistance.

2021 ◽  
Author(s):  
Qingyun Zou ◽  
Xiaoxin Cui ◽  
Zhenhui Dai ◽  
Yisong Kuang ◽  
Yi Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Side Channel ◽  
Side Channel Attack

scholarly journals A Novel Machine Learning Model for Assisting People with Motor Deficiencies

2021 ◽  
Vol 56 (1) ◽  
Author(s):  
Farid Ali Mousa ◽  
Ibrahim Eldesouky Fattoh
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Feature Vector ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Discrete Wavelet ◽  
Proposed Model ◽  
Wide Range ◽  
Four Levels

Motor defects are a major problem affecting millions of people around the world. These individuals suffer from weakness in day-to-day functioning, which can lead to decreased and incoherent daily routines and impair their quality of life. This research describes a new machine learning-based model intended to help individuals with limb motor disabilities using their brain signals to control assistive devices in their daily life activities. The proposed model uses Empirical Mode Decomposition for removing the artifacts of the electroencephalography (EEG) signal, a modified Principal Component Analysis to reduce the input channels, and wavelet transform to extract features. In this experiment, discrete wavelet transform was used to decompose the signal at four levels. The approximate coefficient Ca and all level detail coefficients Cd4, Cd3, Cd2, and Cd1 were used to get the feature vector. All previous coefficients were used as input to Independent Component Analysis for feature reduction. Many amplitude estimators for neurological activities were defined mathematically to get the feature vector; finally, we classified the data using an artificial neural network. The proposed model evaluation was confirmed by testing on three different benchmark datasets, and the resulted accuracy of the proposed model was 88.067%, which outperforms a wide range of many current approaches.

Masked FPGA Bitstream Encryption via Partial Reconfiguration

2019 ◽  
Vol 28 (03n04) ◽  
pp. 1940022
Author(s):  
Yanping Gong ◽  
Fengyu Qian ◽  
Lei Wang
Keyword(s):  
Side Channel ◽  
Light Weight ◽  
Partial Reconfiguration ◽  
Hardware Complexity ◽  
Side Channel Attacks ◽  
Gate Arrays ◽  
Critical Design ◽  
Iot Applications ◽  
Field Programmable ◽  
Programmable Gate Arrays

Field Programmable Gate Arrays (FPGA), as one of the popular circuit implementation platforms, provide the flexible and powerful way for different applications. IC designs are configured to FPGA through bitstream files. However, the configuration process can be hacked by side channel attacks (SCA) to acquire the critical design information, even under the protection of encryptions. Reports have shown many successful attacks against the FPGA cryptographic systems during the bitstream loading process to acquire the entire design. Current countermeasures, mostly random masking methods, are effective but also introduce large hardware complexity. They are not suitable for resource-constrained scenarios such as Internet of Things (IoT) applications. In this paper, we propose a new secure FPGA masking scheme to counter the SCA. By utilizing the FPGA partial reconfiguration feature, the proposed technique provides a light-weight and flexible solution for the FPGA decryption masking.

An Enhanced Sentiment Analysis Framework Based on Pre-Trained Word Embedding

2020 ◽  
Vol 19 (04) ◽  
pp. 2050031 ◽  
Author(s):  
Ensaf Hussein Mohamed ◽  
Mohammed ElSaid Moussa ◽  
Mohamed Hassan Haggag
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Ensemble Classifier ◽  
Word Embedding ◽  
Machine Learning Techniques ◽  
Bag Of Words ◽  
Pos Tagging ◽  
Learning Techniques ◽  
Proposed Model ◽  
Machine Learning Approach

Sentiment analysis (SA) is a technique that lets people in different fields such as business, economy, research, government, and politics to know about people’s opinions, which greatly affects the process of decision-making. SA techniques are classified into: lexicon-based techniques, machine learning techniques, and a hybrid between both approaches. Each approach has its limitations and drawbacks, the machine learning approach depends on manual feature extraction, lexicon-based approach relies on sentiment lexicons that are usually unscalable, unreliable, and manually annotated by human experts. Nowadays, word-embedding techniques have been commonly used in SA classification. Currently, Word2Vec and GloVe are some of the most accurate and usable word embedding techniques, which can transform words into meaningful semantic vectors. However, these techniques ignore sentiment information of texts and require a huge corpus of texts for training and generating accurate vectors, which are used as inputs of deep learning models. In this paper, we propose an enhanced ensemble classifier framework. Our framework is based on our previously published lexicon-based method, bag-of-words, and pre-trained word embedding, first the sentence is preprocessed by removing stop-words, POS tagging, stemming and lemmatization, shortening exaggerated word. Second, the processed sentence is passed to three modules, our previous lexicon-based method (Sum Votes), bag-of-words module and semantic module (Word2Vec and Glove) and produced feature vectors. Finally, the previous features vectors are fed into 11 different classifiers. The proposed framework is tested and evaluated over four datasets with five different lexicons, the experiment results show that our proposed model outperforms the previous lexicon based and the machine learning methods individually.

Sign in / Sign up

Export Citation Format

Share Document