scholarly journals Optimized S-box and Mix-Columns for AES Architecture for live IP video Encryption and Decryption by Key Generation Through face Recognition

2019 ◽  
Vol 8 (10) ◽  
pp. 2941-2945
Keyword(s):  
Principal Component ◽  
Original Video ◽  
Surveillance Systems ◽  
Key Generation ◽  
Pipelined Architecture ◽  
The Neural Network ◽  
Implementation Techniques ◽  
Encryption And Decryption ◽  
Vector Algorithms ◽  
Ip Video

This paper proposes an AES based Encryption and Decryption of a live IP video used for security in surveillance systems. Here, the key is generated based on neural networks techniques for facial recognition. Principal component analysis and Eigen vector algorithms are used to extract biometric facial features which are used to train the neural network. At the receiver side, the original video plays only if the user is authenticated or else it plays an encrypted video. This work proposes an AES architecture based on optimizing timing in terms of adding inner and outer pipeline registers for each round and Key Expansions. Further by optimizing the Crypto Multiplication for Mix columns via LUT based approach aid in further optimization in terms of timing. LUT and Pipelined based implementation techniques are optimal for FPGA based implementations. ROM table and pipelining are the two techniques used to implement AES. Result indicates that with the combination of pipelined architecture and Distributed/Split LUTPipelined techniques, the encryption has higher throughput and speed

scholarly journals Provide a New Encryption Algorithm for Medical Images and Evaluate the Proposed Algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 2
Author(s):  
Mehdi Lotfi ◽  
Hossein Kheiri ◽  
Azizeh Jabbari
Keyword(s):  
Differential Equations ◽  
Chaotic Systems ◽  
Medical Information ◽  
Medical Images ◽  
Encryption Algorithm ◽  
Key Generation ◽  
Time Period ◽  
Key Space ◽  
Encryption And Decryption ◽  
Very High

Introduction:  In this paper, an encryption algorithm for the security of medical images is presented, which has extraordinary security. Given that the confidentiality of patient data is one of the priorities of medical informatics, the algorithm can be used to store and send medical image.Material and Methods:  In this paper, the solutions of chaotic differential equations are used to generate encryption keys. This method is more than other methods used in encoding medical images, resistant to statistics attacks, low encryption and decryption time and very high key space. In the proposed algorithm, unlike other methods that use random key generation, this method uses the production of solutions of the chaotic differential equations in a given time period for generating a key. All simulations and coding are done in MATLAB software.Results:   Chaotic Differential Equations have two very important features that make it possible to encode medical images. One is the unpredictability of the system's behavior and the other is a severe sensitivity to the initial condition.Conclusion: These two features make the method resistant to possible attacks to decode the concept of synchronization chaotic systems. Using the results of the method, medical information can be made safer than existing ones.

scholarly journals RSA algorithm using key generator ESRKGS to encrypt chat messages with TCP/IP protocol

2020 ◽  
Vol 8 (2) ◽  
pp. 113-120
Author(s):  
Aminudin Aminudin ◽  
Gadhing Putra Aditya ◽  
Sofyan Arifianto
Keyword(s):  
Prime Number ◽  
Instant Messaging ◽  
Prime Numbers ◽  
Public Key ◽  
Key Generation ◽  
Security Testing ◽  
Rsa Algorithm ◽  
Computer Resource ◽  
Encryption And Decryption ◽  
Private And Public

This study aims to analyze the performance and security of the RSA algorithm in combination with the key generation method of enhanced and secured RSA key generation scheme (ESRKGS). ESRKGS is an improvement of the RSA improvisation by adding four prime numbers in the property embedded in key generation. This method was applied to instant messaging using TCP sockets. The ESRKGS+RSA algorithm was designed using standard RSA development by modified the private and public key pairs. Thus, the modification was expected to make it more challenging to factorize a large number n into prime numbers. The ESRKGS+RSA method required 10.437 ms faster than the improvised RSA that uses the same four prime numbers in conducting key generation processes at 1024-bit prime number. It also applies to the encryption and decryption process. In the security testing using Fermat Factorization on a 32-bit key, no prime number factor was found. The test was processed for 15 hours until the test computer resource runs out.

Implementasi Algoritma Merkle-Hellman Knapsack dalam Penyandian Record Database

2020 ◽  
pp. 162-165
Author(s):  
Reni Rahmadani ◽  
Harvei Desmon Hutahaean ◽  
Ressy Dwitias Sari
Keyword(s):  
Data Security ◽  
Public Key Cryptography ◽  
Data Encryption ◽  
Key Generation ◽  
Software Developers ◽  
Data Owner ◽  
Encryption And Decryption ◽  
User Data ◽  
Np Complete ◽  
Knapsack Algorithm

A lot of data is misused without the data owner being aware of it. Software developers must ensure the security user data on their system. Due to the size of the market that houses data, the security of record databases must be of great concern. Cryptographic systems or data encryption can be used for data security. The Merkle-Hellman Knapsack algorithm is included in public-key cryptography because it uses different keys for the encryption and decryption processes. This algorithm belongs to the NP-complete algorithm which cannot be solved in polynomial order time. This algorithm has stages of key generation, encryption, and decryption. The results of this study secure database records from theft by storing records in the form of ciphertext/password. Ciphertext generated by algorithmic encryption has a larger size than plaintext.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

Hybrid Cryptography for Random-key Generation based on ECC Algorithm

2017 ◽  
Vol 7 (3) ◽  
pp. 1293 ◽  
Author(s):  
P. Gayathri ◽  
Syed Umar ◽  
G. Sridevi ◽  
N. Bashwanth ◽  
Royyuru Srikanth
Keyword(s):  
Security Level ◽  
Key Generation ◽  
Public Key Encryption ◽  
Private Key ◽  
Cryptographic Algorithm ◽  
Encryption And Decryption ◽  
Safe Side ◽  
Key Length ◽  
High Level ◽  
User Friendly

As more increase in usage of communications and developing them more user friendly. While developing those communications, we need to take care of security and safety of user’s data. Many researchers have developed many complex algorithms to maintain security in user’s application. Among those one of the best algorithms are cryptography based, in which user will be safe side mostly from the attackers.  We already had some AES algorithm which uses very complex cryptographic algorithm to increase the performance and more usage of lookup tables. So the cache timing attackers will correlates the details to encrypt the data under known key with the unknown key. So, for this we provide an improvised solution. This paper deals with an extension of public-key encryption and decryption support including a private key. The private key is generated with the combination of AES and ECC. In general AES, key length is 128 bits with 10 times of iterations. But with this, users won’t get efficient security for their operations, so to increase the security level we are implementing 196-bit based encryption with 12 times round-key generation iterations. By this enhancement, we can assure to users to high level security and can keep users data in confidential way.

Implementation of Reduce AI-NN Model for Highly Accurate Blood Pressure Measurement

2020 ◽  
Author(s):  
Jerry Lin ◽  
Rajeev Kumar Pandey ◽  
Paul C.-P. Chao
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Measurement Accuracy ◽  
Blood Pressure Measurement ◽  
Principal Component ◽  
Data Sets ◽  
British Hypertension Society ◽  
The Neural Network ◽  
Pca Method ◽  
Highly Correlated

Abstract This study proposes a reduce AI model for the accurate measurement of the blood pressure (BP). In this study varied temporal periods of photoplethysmography (PPG) waveforms is used as the features for the artificial neural networks to estimate blood pressure. A nonlinear Principal component analysis (PCA) method is used herein to remove the redundant features and determine a set of dominant features which is highly correlated to the Blood pressure (BP). The reduce features-set not only helps to minimize the size of the neural network but also improve the measurement accuracy of the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The designed Neural Network has the 5-input layer, 2 hidden layers (32 nodes each) and 2 output nodes for SBP and DBP, respectively. The NN model is trained by the PPG data sets, acquired from the 96 subjects. The testing regression for the SBP and DBP estimation is obtained as 0.81. The resultant errors for the SBP and DBP measurement are 2.00±6.08 mmHg and 1.87±4.09 mmHg, respectively. According to the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standard, the measured error of ±6.08 mmHg is less than 8 mmHg, which shows that the device performance is in grade “A”.

scholarly journals Barrel Shifter Physical Unclonable Function Based Encryption

Cryptography ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 22 ◽  
Author(s):  
Yunxi Guo ◽  
Timothy Dee ◽  
Akhilesh Tyagi
Keyword(s):  
Logistic Regression ◽  
Manufacturing Process ◽  
Ring Oscillator ◽  
Key Generation ◽  
Physical Unclonable Functions ◽  
Encryption And Decryption ◽  
Barrel Shifter ◽  
Transmission Gates ◽  
Encryption Decryption ◽  
Randomness Tests

Physical Unclonable Functions (PUFs) are designed to extract physical randomness from the underlying silicon. This randomness depends on the manufacturing process. It differs for each device. This enables chip-level authentication and key generation applications. We present an encryption protocol using PUFs as primary encryption/decryption functions. Each party has a PUF used for encryption and decryption. This PUF is constrained to be invertible and commutative. The focus of the paper is an evaluation of an invertible and commutative PUF based on a primitive shifting permutation network—a barrel shifter. Barrel shifter (BS) PUF captures the delay of different shift paths. This delay is entangled with message bits before they are sent across an insecure channel. BS-PUF is implemented using transmission gates for physical commutativity. Post-layout simulations of a common centroid layout 8-level barrel shifter in 0.13 μ m technology assess uniqueness, stability, randomness and commutativity properties. BS-PUFs pass all selected NIST statistical randomness tests. Stability similar to Ring Oscillator (RO) PUFs under environmental variation is shown. Logistic regression of 100,000 plaintext–ciphertext pairs (PCPs) fails to successfully model BS-PUF behavior.

Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals

2020 ◽  
Vol 234 (8) ◽  
pp. 794-811
Author(s):  
Harikrishna Mulam ◽  
Malini Mudigonda
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Principal Component ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Positive Rate ◽  
Sensitivity Specificity

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human–computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg–Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

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