Elliptic Curve and Homomorphic ElGamal Hybrid Based Mutual Authentication Framework for Security in Cloud Computing

2020 ◽  
Vol 9 (5) ◽  
pp. 2309-2316
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Elliptic Curve ◽  
Mutual Authentication ◽  
Computational Time ◽  
Key Generation ◽  
Remote Server ◽  
Encryption And Decryption ◽  
Remote Services ◽  
Agreement Protocols

Cloud computing is an extensive technology from which the client could access several services through a remote server. Authentication of the remote services needs a common key between the client and the server in a secured manner. The existing key agreement protocols utilized various techniques for the preservation of the data security. The proposed work attempted to provide a high secure data by optimizing the key generation for encryption and decryption process. Hybridization of a novel ECC (Elliptic Curve Cryptography) algorithm and homomorphic ElGamal algorithm for encryption and decryption and also employment of a bio-inspired Genetic algorithm for the generation of maximum secured key is performed. A random 128 bit hash have been developed for the generation of two input points to run the homomorphic property in the ElGamal algorithm secures the key and make it as a non breakable one. The integrated property of ECC and the ElGamal associated with the Genetic algorithm makes the key more stabilized and a confidential one that is more resistant to the various kinds of attacks. The performance analysis depicted that the proposed technique outperform the existing with respect to computational time.

scholarly journals A lightweight secure CoAP for IoT-cloud paradigm using Elliptic-curve cryptography

2020 ◽  
Vol 20 (3) ◽  
pp. 1460
Author(s):  
Amrani Ayoub ◽  
Rafalia Najat ◽  
Abouchabaka Jaafar
Keyword(s):  
Cloud Computing ◽  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Mutual Authentication ◽  
Future Internet ◽  
New Paradigm ◽  
The Future ◽  
Secure Data Transmission ◽  
Iot Devices ◽  
The Internet Of Things

<span>Cloud Computing and the Internet of Things (IoT), two different technologies, are already part of our lives. Their impressive adoption increasing more and more, which makes them the future of the future internet. The tsunami of interconnectivity between objects and data collection is increasingly based on Cloud Computing, where data analysis and intelligence really reside. A new paradigm where the Cloud and the IoT are merged will create a new air in the world of technology, which can offer many services and applications useful to humanity. However, despite the great benefits that can bring this technology in term of new services, elasticity and flexibility, the security aspect still remains a serious constraint which hampers the expansion of this technology. This paper proposes a lightweight Mutual authentication protocol based on Constrained Application Protocol (CoAP); that is suitable for IoT devices than HTTP and using elliptic curve cryptography to secure data transmission between the Cloud and devices. We used the AVISPA tool to verify our proposed scheme.</span>

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Enhanced Security Authentication of WiMAX using EC3A

2017 ◽  
Vol 7 (8) ◽  
pp. 219
Author(s):  
Mohd Javed ◽  
Khaleel Ahmad ◽  
Ahmad Talha Siddiqui
Keyword(s):  
Cellular Automata ◽  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Information Rate ◽  
Security Authentication ◽  
Encryption And Decryption ◽  
High Information

WiMAX is the innovation and upgradation of 802.16 benchmarks given by IEEE. It has numerous remarkable qualities, for example, high information rate, the nature of the service, versatility, security and portability putting it heads and shoulder over the current advancements like broadband link, DSL and remote systems. Though like its competitors the concern for security remains mandatory. Since the remote medium is accessible to call, the assailants can undoubtedly get into the system, making the powerless against the client. Many modern confirmations and encryption methods have been installed into WiMAX; however, regardless it opens with up different dangers. In this paper, we proposed Elliptic curve Cryptography based on Cellular Automata (EC3A) for encryption and decryption the message for improving the WiMAX security

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Public-Key Encryption

2017 ◽  
Author(s):  
Keith M. Martin
Keyword(s):  
Elliptic Curve ◽  
Public Key Cryptography ◽  
Public Key ◽  
Public Key Encryption ◽  
Hybrid Encryption ◽  
Public Key Cryptosystems ◽  
Encryption And Decryption ◽  
Encryption Schemes ◽  
Hard Problems ◽  
Set Up

In this chapter, we introduce public-key encryption. We first consider the motivation behind the concept of public-key cryptography and introduce the hard problems on which popular public-key encryption schemes are based. We then discuss two of the best-known public-key cryptosystems, RSA and ElGamal. For each of these public-key cryptosystems, we discuss how to set up key pairs and perform basic encryption and decryption. We also identify the basis for security for each of these cryptosystems. We then compare RSA, ElGamal, and elliptic-curve variants of ElGamal from the perspectives of performance and security. Finally, we look at how public-key encryption is used in practice, focusing on the popular use of hybrid encryption.

Optimal machine placement based on improved genetic algorithm in cloud computing

2021 ◽  
Author(s):  
Jiawei Lu ◽  
Wei Zhao ◽  
Haotian Zhu ◽  
Jie Li ◽  
Zhenbo Cheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Improved Genetic Algorithm ◽  
Optimal Machine

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

Sign in / Sign up

Export Citation Format

Share Document