scholarly journals Genetic Algorithm for Testing Web Applications

2018 ◽  
Author(s):  
Nashat Mansour ◽  
Ramzi Haraty ◽  
Hratch Zeitunlian
Keyword(s):  
Genetic Algorithm ◽  
Web Applications

scholarly journals A hybrid method of genetic algorithm and support vector machine for DNS tunneling detection

2021 ◽  
Vol 11 (2) ◽  
pp. 1666
Author(s):  
Fuqdan A. Al-Ibraheemi ◽  
Sattar AL-Ibraheemi ◽  
Haleh Amintoosi
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Hybrid Method ◽  
Web Applications ◽  
Content Volume ◽  
Geographic Location ◽  
Support Vector ◽  
Detection Approach ◽  
Feature Selection Approach

With the expansion of the business over the internet, corporations nowadays are investing numerous amounts of money in the web applications. However, there are different threats could make the corporations vulnerable for potential attacks. One of these threats is harnessing the domain name protocol for passing harmful information, this kind of threats is known as DNS tunneling. As a result, confidential information would be exposed and violated. Several studies have investigated the machine learning in order to propose a detection approach. In their approaches, authors have used different and numerous types of features such as domain length, number of bytes, content, volume of DNS traffic, number of hostnames per domain, geographic location and domain history. Apparently, there is a vital demand to accommodate feature selection task in order to identify the best features. This paper proposes a hybrid method of genetic algorithm feature selection approach with the support vector machine classifier for the sake of identifying the best features that have the ability to optimize the detection of DNS tunneling. To evaluate the proposed method, a benchmark dataset of DNS tunneling has been used. Results showed that the proposed method has outperformed the conventional SVM by achieving 0.946 of f-measure

scholarly journals Optimization Method of Web Fuzzy Test Cases Based on Genetic Algorithm

2021 ◽  
Vol 2078 (1) ◽  
pp. 012015
Author(s):  
Sheng Qu ◽  
Zheng Zhang ◽  
Bolin Ma ◽  
Yuwen Shao
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Web Service ◽  
Web Applications ◽  
Optimization Method ◽  
Test Cases ◽  
Public Network ◽  
Code Coverage ◽  
Signature Database ◽  
The Web

Abstract In order to solve the problems of low code coverage, few vulnerabilities found, and poor fuzzing effect caused by the small number of test cases and single types in Web fuzzing, on the basis of studying the current Web fuzzing methods, the existing fuzzing Web applications are tested Program research. A genetic algorithm-based method for optimizing fuzzing test cases for Web applications is proposed. It analyzes and counts the traffic of public network website business with Web service attack characteristics, and uses genetic algorithms to generate a large number of test cases with various types to explore the Web service vulnerability that exists. Based on the creation of a Web attack signature database with weights, this method uses genetic algorithms to randomly pre-generate the test cases of the fuzzing test, and uses the response of the Web service to repeatedly iterate the weights of different attack signatures in the Web attack signature database. So as to generate the best test cases. Experimental analysis shows that this method effectively finds security vulnerabilities in Web applications.

scholarly journals Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure

2015 ◽  
Vol 27 (8) ◽  
pp. 2383-2406 ◽  
Author(s):  
Valter Rogério Messias ◽  
Julio Cezar Estrella ◽  
Ricardo Ehlers ◽  
Marcos José Santana ◽  
Regina Carlucci Santana ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Web Applications ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Cloud Infrastructure

scholarly journals Design of Warship Simulation Using Variable-Chromosome Genetic Algorithm

2019 ◽  
Vol 9 (19) ◽  
pp. 4131 ◽  
Author(s):  
Kang-moon Park ◽  
Suk-hoon Shin ◽  
Donghoon Shin ◽  
Sung-do Chi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Web Applications ◽  
Search Algorithm ◽  
Defense Industry ◽  
Industrial Applications ◽  
Fixed Number ◽  
Global Search Algorithm ◽  
Chromosome Attachment

A genetic algorithm (GA) is a global search algorithm based on biological genetics. GAs are generally used for industrial applications, artificial neural networks, web applications, the defense industry, and so on. However, it is difficult to apply GAs to more complex situations because of the fixed number of chromosomes. In this research, in order to overcome this limitation, we propose a variable-chromosome GA with a chromosome attachment feature. Verification of the algorithm is carried out through anti-submarine high value unit (HVU) escort mission simulations. Ultimately, it is confirmed that the GA using the variable chromosome is more effective in dealing with highly complex missions, whereby the number of chromosomes gradually increases.

A Multi-Objective Approach for Test Suite Reduction During Testing of Web Applications

2021 ◽  
Vol 12 (3) ◽  
pp. 81-122
Author(s):  
Munish Khanna ◽  
Naresh Chauhan ◽  
Dilip Kumar Sharma ◽  
Law Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Web Applications ◽  
State Of The Art ◽  
Test Suite ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Reduction Problem ◽  
Test Suite Reduction ◽  
Experimental Findings

During the development and maintenance phases of evolving software, new test cases would be needed for the verification of the accuracy of the modifications as well as for new functionalities leading to an increase in the size of the test suite. Various related objectives are to be kept in mind while reducing the original test suite by removing redundancy and generating a practical representative set of the unique test cases, some of which may need to be maximized and the remaining ones minimized. This paper presents a multi-objective approach for the test suite reduction problem in which one objective is to be minimized and the remaining two maximized. In this study, experiments were performed on diverse versions of four web applications. Various state-of-the-art algorithms and their updated versions were compared with non-dominated sorting genetic algorithm-II (NSGA-II) for performance evaluation. Based on experimental findings, it was concluded that NSGA-II outperforms all other algorithms; moreover, the algorithm attempts to satisfy all the objectives without compromising coverage.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy
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