Generative Neural Networks as a Tool for Web Applications Penetration Testing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F24%3A00560793" target="_blank" >RIV/60162694:G43__/24:00560793 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10296970" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10296970</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/KIT59097.2023.10297109" target="_blank" >10.1109/KIT59097.2023.10297109</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Generative Neural Networks as a Tool for Web Applications Penetration Testing

Popis výsledku v původním jazyce

The scientific paper delves into the potential of generative neural networks as a powerful tool for web application penetration testing. By leveraging the capabilities of these networks, we aim to augment traditional testing methodologies and advance the field of vulnerability detection. In the second section, the paper provides an overview of OpenAI, a leading organization at the forefront of artificial intelligence research and development. OpenAI has contributed significantly to the field of natural language processing and has developed advanced models like ChatGPT, which have the potential to revolutionize various industries, including cybersecurity. It explores the underlying technology behind ChatGPT and discusses its implications for the field of web application penetration testing. Third section focuses on present the details of the experimental setup. A series of three experiments was conducted to evaluate the effectiveness of generative neural networks, specifically ChatGPT, in web application penetration testing. Through these experiments, its aim was to demonstrate the practical application of generative neural networks in identifying and exploiting web-based security vulnerabilities. In the fourth section, the results obtained from the experiments are presented. Parts of the experiment were categorized into three sub-results, each highlighting a specific aspect of vulnerability detection. The main intention was to highlight the potential of generative neural networks as an innovative and effective tool for web application penetration testing. In conclusion, the paper showcases the advancements made possible by generative neural networks in the domain of web application penetration testing. By automating certain aspects of the testing process and enhancing vulnerability detection, these networks hold immense promise for improving the overall security posture of web-based systems. The findings presented in this paper contribute to the growing body of knowledge in the field and open up new avenues for further research and development in this critical area of cybersecurity.

Název v anglickém jazyce

Generative Neural Networks as a Tool for Web Applications Penetration Testing

Popis výsledku anglicky

The scientific paper delves into the potential of generative neural networks as a powerful tool for web application penetration testing. By leveraging the capabilities of these networks, we aim to augment traditional testing methodologies and advance the field of vulnerability detection. In the second section, the paper provides an overview of OpenAI, a leading organization at the forefront of artificial intelligence research and development. OpenAI has contributed significantly to the field of natural language processing and has developed advanced models like ChatGPT, which have the potential to revolutionize various industries, including cybersecurity. It explores the underlying technology behind ChatGPT and discusses its implications for the field of web application penetration testing. Third section focuses on present the details of the experimental setup. A series of three experiments was conducted to evaluate the effectiveness of generative neural networks, specifically ChatGPT, in web application penetration testing. Through these experiments, its aim was to demonstrate the practical application of generative neural networks in identifying and exploiting web-based security vulnerabilities. In the fourth section, the results obtained from the experiments are presented. Parts of the experiment were categorized into three sub-results, each highlighting a specific aspect of vulnerability detection. The main intention was to highlight the potential of generative neural networks as an innovative and effective tool for web application penetration testing. In conclusion, the paper showcases the advancements made possible by generative neural networks in the domain of web application penetration testing. By automating certain aspects of the testing process and enhancing vulnerability detection, these networks hold immense promise for improving the overall security posture of web-based systems. The findings presented in this paper contribute to the growing body of knowledge in the field and open up new avenues for further research and development in this critical area of cybersecurity.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 Communication and Information Technologies, KIT 2023 - 12th International Scientific Conference, Proceedings

ISBN

979-8-3503-3839-3

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Vysoke Tatry, Slovak Republic

Datum konání akce

11. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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