Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems
Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/211 |
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| author | Ariadna Claudia Moreno Aldo Hernandez-Suarez Gabriel Sanchez-Perez Linda Karina Toscano-Medina Hector Perez-Meana Jose Portillo-Portillo Jesus Olivares-Mercado Luis Javier García Villalba |
| author_facet | Ariadna Claudia Moreno Aldo Hernandez-Suarez Gabriel Sanchez-Perez Linda Karina Toscano-Medina Hector Perez-Meana Jose Portillo-Portillo Jesus Olivares-Mercado Luis Javier García Villalba |
| author_sort | Ariadna Claudia Moreno |
| collection | DOAJ |
| description | Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives. To enhance the effectiveness of these tests, Machine Learning (ML) has been integrated, showing significant potential for identifying anomalies across various security areas through detailed detection of underlying malicious patterns. However, pentesting environments are unpredictable and intricate, requiring analysts to make extensive efforts to understand, explore, and exploit them. This study considers these challenges, proposing a recommendation system based on a context-rich, vocabulary-aware transformer capable of processing questions related to the target environment and offering responses based on necessary pentest batteries evaluated by a Reinforcement Learning (RL) estimator. This RL component assesses optimal attack strategies based on previously learned data and dynamically explores additional attack vectors. The system achieved an F1 score and an Exact Match rate over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>97.0</mn></mrow></semantics></math></inline-formula>%, demonstrating its accuracy and effectiveness in selecting relevant pentesting strategies. |
| format | Article |
| id | doaj-art-72b8277cba2642eaa2f3c5c06af36375 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-72b8277cba2642eaa2f3c5c06af363752025-08-20T02:47:06ZengMDPI AGSensors1424-82202025-01-0125121110.3390/s25010211Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender SystemsAriadna Claudia Moreno0Aldo Hernandez-Suarez1Gabriel Sanchez-Perez2Linda Karina Toscano-Medina3Hector Perez-Meana4Jose Portillo-Portillo5Jesus Olivares-Mercado6Luis Javier García Villalba7Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, MexicoInstituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, MexicoInstituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, MexicoInstituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, MexicoInstituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, MexicoInstituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, MexicoInstituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, MexicoGroup of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, SpainConducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives. To enhance the effectiveness of these tests, Machine Learning (ML) has been integrated, showing significant potential for identifying anomalies across various security areas through detailed detection of underlying malicious patterns. However, pentesting environments are unpredictable and intricate, requiring analysts to make extensive efforts to understand, explore, and exploit them. This study considers these challenges, proposing a recommendation system based on a context-rich, vocabulary-aware transformer capable of processing questions related to the target environment and offering responses based on necessary pentest batteries evaluated by a Reinforcement Learning (RL) estimator. This RL component assesses optimal attack strategies based on previously learned data and dynamically explores additional attack vectors. The system achieved an F1 score and an Exact Match rate over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>97.0</mn></mrow></semantics></math></inline-formula>%, demonstrating its accuracy and effectiveness in selecting relevant pentesting strategies.https://www.mdpi.com/1424-8220/25/1/211penetration testingreinforcement learningrecommender systems |
| spellingShingle | Ariadna Claudia Moreno Aldo Hernandez-Suarez Gabriel Sanchez-Perez Linda Karina Toscano-Medina Hector Perez-Meana Jose Portillo-Portillo Jesus Olivares-Mercado Luis Javier García Villalba Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems Sensors penetration testing reinforcement learning recommender systems |
| title | Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems |
| title_full | Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems |
| title_fullStr | Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems |
| title_full_unstemmed | Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems |
| title_short | Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems |
| title_sort | analysis of autonomous penetration testing through reinforcement learning and recommender systems |
| topic | penetration testing reinforcement learning recommender systems |
| url | https://www.mdpi.com/1424-8220/25/1/211 |
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