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Preprint
Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
Cornell University, version1
09/13/2025
Abstract
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.
Files and links (1)
Details
- Title
- Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
- Edition
- version1
- Resource Type
- Preprint
- Publisher
- Cornell University; arXiv
- Format
- link
- Number of pages
- 9
- Identifiers
- 99381500019006600
- Academic Unit
- Mathematics and Statistics; Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English