<link rel="stylesheet" href="styles.f3b1fba60ec7970c.css">

Застосування штучного інтелекту для тестування на проникнення

Вантажиться...
Ескіз

Дата

Назва журналу

Номер ISSN

Назва тому

DOI

Анотація

This paper explores artificial intelligence (AI) utilization for penetration testing in modern cybersecurity. The study meticulously analyzes the advantages and disadvantages of employing AI in this domain. The main advantages of using AI include its ability to enhance efficiency, accuracy, and automation of threat detection processes. Specifically, the potential development of automated systems for detecting and addressing cyber-attacks through AI is discussed. Special attention is paid to the prospects of this technology, including its capability to address complex penetration testing tasks and identify new types of cyber threats. The paper also acknowledges the key disadvantages of applying AI in cybersecurity, such as the risk of errors and concerns regarding data confidentiality. This study highlights AI's significant potential to elevate cybersecurity levels and ensure safety in cyberspace. Furthermore, it underscores the importance of further research and development of this technology for effective combat against cyber threats.

Опис

УДК

Тип документа

Мова

ISSN

Бібліографічний опис

Притула А. В., Куперштейн Л. М. Застосування штучного інтелекту для тестування на проникнення // Матеріали LІII науково-технічної конференції підрозділів ВНТУ, Вінниця, 20-22 березня 2024 р. Електрон. текст. дані. 2024. URI: https://conferences.vntu.edu.ua/index.php/all-fitki/all-fitki-2024/paper/view/20606.

Схвалення

Рецензія

Доповнено

Цитується в

Список використаної літератури (9)

  1. Statista - the statistics portal. Statista. URL: https://www.statista.com/ (дата звернення: 12.03.2024).
  2. Remote host operation system type detection based on machine learning approach / L. Kupershtein et al. Selected papers of the II international scientific symposium "intelligent solutions" (intsol-2021). workshop proceedings. 2022. No. 3106. P. 65–81. URL: https://ir.lib.vntu.edu.ua/handle/123456789/37675 (дата звернення: 12.03.2024).
  3. How artificial intelligence will drive the future of penetration testing in IT security - cybersecurity | digital forensics | penetration testing | ermprotect. Cybersecurity | Digital Forensics | Penetration Testing | ERMProtect - Cybersecurity | Digital Forensics | Penetration Testing | ERMProtect. URL: https://ermprotect.com/blog/how-artificial-intelligence-will-drive-the-future-of-penetrationtesting/ (дата заверення: 12.03.2024).
  4. Pope J. Human vs AI in pen testing. Cybersmart consulting. URL: https://cybersmartconsulting.com/ai-in-pen-testing/ (дата зверенення: 12.03.2024).
  5. Investigating threats with watson for cyber security - security intelligence. Security Intelligence. URL: https://securityintelligence.com/investigating-threats-with-watson-for-cyber-security/ (дата звернення: 12.03.2024).
  6. The 2020 global CVC report. CB Insights. URL: https://www.cbinsights.com/research/report/corporate-venture-capital-trends2020/ (date of access: 13.03.2024).
  7. Ijlal T. How to start penetration testing of artificial intelligence. Infosec writeups. URL: https://infosecwriteups.com/how-to-startpenetration-testing-of-artificial-intelligence-c11e97b77dfa (дата звернення: 13.03.2024).
  8. Küçükkarakurt F. Is it possible to use artificial intelligence for penetration tests?. Make use of. URL: https://www.makeuseof.com/is-it-possible-to-use-artificial-intelligence-for-penetration-tests/ (дата звернення: 13.03.2024).
  9. Joseph T. The influence of AI and machine learning on pen testing. QASource Blog. URL: https://blog.qasource.com/the-influenceof-ai-and-machine-learning-on-pen-testing (дата зверення: 14.03.2024).