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Analysis Of Autonomous Penetration Testing Through Reinforcement Learning And Recommender Systems.

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Analysis Of Autonomous Penetration Testing Through Reinforcement Learning And Recommender Systems.

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Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems.

Ariadna Claudia Moreno¹, Aldo Hernandez-Suarez¹, Gabriel Sanchez-Perez¹

¹Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico.

Sensors (Basel, Switzerland)

|January 11, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an advanced recommendation system for cybersecurity penetration testing (pentesting). It uses machine learning and reinforcement learning to improve vulnerability detection accuracy and optimize attack strategies.

Keywords:

penetration testing recommender systems reinforcement learning

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Area of Science:

Cybersecurity
Machine Learning
Artificial Intelligence

Background:

Penetration testing (pentesting) is vital for identifying IT vulnerabilities but faces challenges like false positives from analysis tools.
Existing pentesting methods require significant analyst expertise due to complex and unpredictable environments.
Machine Learning (ML) shows promise in anomaly detection but requires integration into dynamic pentesting workflows.

Purpose of the Study:

To develop an intelligent system that enhances the effectiveness and accuracy of penetration testing.
To address the limitations of current pentesting tools and methodologies.
To propose a novel approach for automated vulnerability assessment and exploitation strategy selection.

Main Methods:

A context-rich, vocabulary-aware transformer model processes questions about the target environment.

A Reinforcement Learning (RL) estimator evaluates and selects optimal pentest strategies.

The system dynamically explores attack vectors based on learned data and environmental context.

Main Results:

The proposed system achieved an F1 score exceeding 97.0%.
An Exact Match rate of over 97.0% was recorded, indicating high accuracy.
Demonstrated effectiveness in selecting relevant and optimal pentesting strategies.

Conclusions:

The developed system significantly improves the accuracy and efficiency of penetration testing.
The integration of ML and RL offers a powerful solution for complex cybersecurity challenges.
This approach enhances the identification of vulnerabilities and strengthens preventive controls in IT systems.