Інструменти штучного інтелекту для автоматизації тестування на проникнення

The article discusses automated penetration testing using artificial intelligence (AI), which significantly enhances the efficiency and accuracy of cybersecurity assessments. AI-based technologies are capable of automating many processes that were previously performed manually, including vulnerability scanning, threat analysis, and exploitation of system weaknesses. Special attention is given to AI-based tools such as DeepExploit, Sn1per, and Cortex XSOAR, which demonstrate substantial advantages over traditional penetration testing methods. The article also addresses the main challenges of implementing AI in penetration testing, including the difficulties of training models and the issue of false positives. The future trends in the use of AI for cybersecurity, such as autonomous testing systems and integration with quantum computing, are explored as well.

Publication Details

Journal
The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy
Published
2024-12-23
Primary Topic
Fusion and Plasma Physics Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Інструменти штучного інтелекту для автоматизації тестування на проникнення

The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy
Fusion and Plasma Physics Studies
article

Інструменти штучного інтелекту для автоматизації тестування на проникнення

article en

Abstract

The article discusses automated penetration testing using artificial intelligence (AI), which significantly enhances the efficiency and accuracy of cybersecurity assessments. AI-based technologies are capable of automating many processes that were previously performed manually, including vulnerability scanning, threat analysis, and exploitation of system weaknesses. Special attention is given to AI-based tools such as DeepExploit, Sn1per, and Cortex XSOAR, which demonstrate substantial advantages over traditional penetration testing methods. The article also addresses the main challenges of implementing AI in penetration testing, including the difficulties of training models and the issue of false positives. The future trends in the use of AI for cybersecurity, such as autonomous testing systems and integration with quantum computing, are explored as well.

The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy
Peace, Justice and strong institutions
Openalex Percentile: Top 59%
Fusion and Plasma Physics Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.