Improving the Security and Authentication of the Cloud with IoT using Hybrid Optimization Based Quantum Hash Function

2020 ◽  
pp. 61-71
Author(s):  
K. Shankar ◽  
Keyword(s):  
Hash Function ◽  
Artificial Bee Colony Algorithm ◽  
Cuckoo Search ◽  
Information Protection ◽  
Iot Applications ◽  
Bee Colony ◽  
Quantum Hash Function ◽  
Cloud Server ◽  
Cloud Framework ◽  
Cloud Servers

The security with the protection of IoT is to stay a consequential test, for the most part, because of the huge scale and dispersed nature of IoT systems. A cloud server brings wide pertinence of IoT in numerous businesses just as Government parts. Be that as it may, the security concerns, for example, verification and information protection of these gadgets assume a key job in fruitful coordination of two innovations. To build the security here, a quantum hash work system and hybrid cuckoo search-Artificial Bee Colony algorithm is displayed. A quantum hash work has been presented as an amazing system for secure correspondence of IoT and cloud because of its irregular disordered robust execution, greater affectability for introductory authority dimension, steadiness, and the exceptionally huge crucial area is hypothetically sufficiently able to oppose different known assaults. Cloud servers utilizing CS-ABC to upgrading the safe calculations through a quantum channel inside the cloud framework. Execution examinations and recreation outcomes demonstrate our presented methods are portrayed and also have greater safety, proficiency with strength opposed to a few surely understood assaults which choose them as a great contender for verifying cloud and IoT applications.

Automatic Test Data Generation Using Bio-Inspired Algorithms

2018 ◽  
pp. 140-159 ◽  
Author(s):  
Madhumita Panda ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A sizable number of Nature based algorithms coming under the per- view of metaheuristics have been used by researchers to solve practical problems of different disciplines of engineering and computer science, and software engineering. Here an exhaustive review of metaheuristic algorithms which have been employed to optimize the solution of test data generation for past 20 -30 years is presented. In addition to this, authors have reviewed their own work has been developed particularly to generate test data for path coverage based testing using Cuckoo Search and Gravitational Search algorithms. Also, an extensive comparison with the results obtained using Genetic Algorithms, Particle swarm optimization, Differential Evolution and Artificial Bee Colony algorithm are presented to establish the significance of the study.

Automatic Test Data Generation Using Bio-Inspired Algorithms

2021 ◽  
pp. 848-866
Author(s):  
Madhumita Panda ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A sizable number of Nature based algorithms coming under the per- view of metaheuristics have been used by researchers to solve practical problems of different disciplines of engineering and computer science, and software engineering. Here an exhaustive review of metaheuristic algorithms which have been employed to optimize the solution of test data generation for past 20 -30 years is presented. In addition to this, authors have reviewed their own work has been developed particularly to generate test data for path coverage based testing using Cuckoo Search and Gravitational Search algorithms. Also, an extensive comparison with the results obtained using Genetic Algorithms, Particle swarm optimization, Differential Evolution and Artificial Bee Colony algorithm are presented to establish the significance of the study.

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