Supervised Machine Learning in Cybersecurity
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| Formato: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901523676528640 |
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| author | Loza, Boris |
| author_facet | Loza, Boris |
| contents | <p><span>The modern field of information security is inseparable from the use of Artificial Intelligence (AI). Both adversaries, such as Advanced Persistent Threat (APT) teams, so-called black hats, and even script kiddies, as well as professional security defense teams rely heavily on machine learning (ML) tools with various models and algorithms. Understanding what ML technology offers to security professionals is essential.<span> </span></span></p> <p><span>This is the first in a series of three articles exploring 16 of the most popular Machine Learning (ML) and Deep Learning (DL) models and algorithms across three main AI categories: Supervised, Unsupervised, and Reinforcement Learning. It also provides an overview of cybersecurity vendors and their products that utilize these ML models. In addition to offering brief descriptions of each model, we analyze the Pros and Cons of each category and their applications in cybersecurity. Finally, we share insights into the strengths and weaknesses of these models when applied to cybersecurity challenges.<span> </span></span></p> <p><span>We hope that after reading this paper, readers will gain a clearer understanding of how AI and ML models can be leveraged in respect to cybersecurity and develop their own perspective on how they may apply to security professional needs.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15633058 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Supervised Machine Learning in Cybersecurity Loza, Boris Supervised Machine Learning cybersecurity machine learning models <p><span>The modern field of information security is inseparable from the use of Artificial Intelligence (AI). Both adversaries, such as Advanced Persistent Threat (APT) teams, so-called black hats, and even script kiddies, as well as professional security defense teams rely heavily on machine learning (ML) tools with various models and algorithms. Understanding what ML technology offers to security professionals is essential.<span> </span></span></p> <p><span>This is the first in a series of three articles exploring 16 of the most popular Machine Learning (ML) and Deep Learning (DL) models and algorithms across three main AI categories: Supervised, Unsupervised, and Reinforcement Learning. It also provides an overview of cybersecurity vendors and their products that utilize these ML models. In addition to offering brief descriptions of each model, we analyze the Pros and Cons of each category and their applications in cybersecurity. Finally, we share insights into the strengths and weaknesses of these models when applied to cybersecurity challenges.<span> </span></span></p> <p><span>We hope that after reading this paper, readers will gain a clearer understanding of how AI and ML models can be leveraged in respect to cybersecurity and develop their own perspective on how they may apply to security professional needs.</span></p> |
| title | Supervised Machine Learning in Cybersecurity |
| topic | Supervised Machine Learning cybersecurity machine learning models |
| url | https://doi.org/10.5281/zenodo.15633058 |