Large Language Models in Cybersecurity: State-of-the-Art
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910315965317120 |
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| author | Motlagh, Farzad Nourmohammadzadeh Hajizadeh, Mehrdad Majd, Mehryar Najafi, Pejman Cheng, Feng Meinel, Christoph |
| author_facet | Motlagh, Farzad Nourmohammadzadeh Hajizadeh, Mehrdad Majd, Mehryar Najafi, Pejman Cheng, Feng Meinel, Christoph |
| contents | The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers have actively explored the applications of LLMs across diverse fields, significantly elevating capabilities. Cybersecurity, traditionally resistant to data-driven solutions and slow to embrace machine learning, stands out as a domain. This study examines the existing literature, providing a thorough characterization of both defensive and adversarial applications of LLMs within the realm of cybersecurity. Our review not only surveys and categorizes the current landscape but also identifies critical research gaps. By evaluating both offensive and defensive applications, we aim to provide a holistic understanding of the potential risks and opportunities associated with LLM-driven cybersecurity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00891 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models in Cybersecurity: State-of-the-Art Motlagh, Farzad Nourmohammadzadeh Hajizadeh, Mehrdad Majd, Mehryar Najafi, Pejman Cheng, Feng Meinel, Christoph Cryptography and Security Artificial Intelligence Computation and Language Machine Learning The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers have actively explored the applications of LLMs across diverse fields, significantly elevating capabilities. Cybersecurity, traditionally resistant to data-driven solutions and slow to embrace machine learning, stands out as a domain. This study examines the existing literature, providing a thorough characterization of both defensive and adversarial applications of LLMs within the realm of cybersecurity. Our review not only surveys and categorizes the current landscape but also identifies critical research gaps. By evaluating both offensive and defensive applications, we aim to provide a holistic understanding of the potential risks and opportunities associated with LLM-driven cybersecurity. |
| title | Large Language Models in Cybersecurity: State-of-the-Art |
| topic | Cryptography and Security Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.00891 |