Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914036103249920 |
|---|---|
| author | Tran, Huynh T. T. Sander, Jacob Cohen, Achraf Jalaian, Brian Bastian, Nathaniel D. |
| author_facet | Tran, Huynh T. T. Sander, Jacob Cohen, Achraf Jalaian, Brian Bastian, Nathaniel D. |
| contents | Transfer learning is commonly utilized in various fields such as computer vision, natural language processing, and medical imaging due to its impressive capability to address subtasks and work with different datasets. However, its application in cybersecurity has not been thoroughly explored. In this paper, we present an innovative neurosymbolic AI framework designed for network intrusion detection systems, which play a crucial role in combating malicious activities in cybersecurity. Our framework leverages transfer learning and uncertainty quantification. The findings indicate that transfer learning models, trained on large and well-structured datasets, outperform neural-based models that rely on smaller datasets, paving the way for a new era in cybersecurity solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection Tran, Huynh T. T. Sander, Jacob Cohen, Achraf Jalaian, Brian Bastian, Nathaniel D. Machine Learning Transfer learning is commonly utilized in various fields such as computer vision, natural language processing, and medical imaging due to its impressive capability to address subtasks and work with different datasets. However, its application in cybersecurity has not been thoroughly explored. In this paper, we present an innovative neurosymbolic AI framework designed for network intrusion detection systems, which play a crucial role in combating malicious activities in cybersecurity. Our framework leverages transfer learning and uncertainty quantification. The findings indicate that transfer learning models, trained on large and well-structured datasets, outperform neural-based models that rely on smaller datasets, paving the way for a new era in cybersecurity solutions. |
| title | Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.10850 |