Artificial intelligence in penetration testing: leveraging AI for advanced vulnerability detection and exploitation

The article examines the ways artificial intelligence is influencing the penetration testing procedure. As technology advances and cyber threats grow more common, conventional testing methods are insufficient. Artificial intelligence aids in automating processes like vulnerability detection and real-world attack simulation, leading to quicker, more precise results with reduced dependence on human input. Machine learning is a game-changer in identifying hidden security flaws by analyzing past attacks and abnormal patterns. Tools mentioned in the article are revolutionizing vulnerability detection, traffic monitoring, and attack simulations. These tools have better key performance metrics, such as scan time, false positive rate, detection accuracy, mean time to detect, zero-day threats/month, compared to traditional penetration testing tools.

Authors

Publication Details

Journal
Advances in Cyber-Physical Systems
Published
2025-05-02
DOI
https://doi.org/10.23939/acps2025.01.065
Citations
2
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
2.97
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article

Artificial intelligence in penetration testing: leveraging AI for advanced vulnerability detection and exploitation

Volodymyr Khoma, Andrian Piskozub, Mariia Kozlovska
2 citations
Advances in Cyber-Physical Systems
Network Security and Intrusion Detection
2.97
article

Artificial intelligence in penetration testing: leveraging AI for advanced vulnerability detection and exploitation

Volodymyr Khoma, Andrian Piskozub, Mariia Kozlovska
article en
2 citations

Abstract

The article examines the ways artificial intelligence is influencing the penetration testing procedure. As technology advances and cyber threats grow more common, conventional testing methods are insufficient. Artificial intelligence aids in automating processes like vulnerability detection and real-world attack simulation, leading to quicker, more precise results with reduced dependence on human input. Machine learning is a game-changer in identifying hidden security flaws by analyzing past attacks and abnormal patterns. Tools mentioned in the article are revolutionizing vulnerability detection, traffic monitoring, and attack simulations. These tools have better key performance metrics, such as scan time, false positive rate, detection accuracy, mean time to detect, zero-day threats/month, compared to traditional penetration testing tools.

Advances in Cyber-Physical SystemsVol. 10(1)
Openalex Percentile: Top 8%
Network Security and Intrusion Detection
2.97
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