Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection

• URL: http://arxiv.org/abs/2509.10850v1
• Date: Sat, 13 Sep 2025 15:12:35 GMT
• Title: Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
• Authors: Huynh T. T. Tran, Jacob Sander, Achraf Cohen, Brian Jalaian, Nathaniel D. Bastian,
• Abstract summary: Transfer learning is commonly utilized in various fields such as computer vision, natural language processing, and medical imaging.<n>We present an innovative neurosymbolic AI framework designed for network intrusion detection systems, which play a crucial role in combating malicious activities in cybersecurity.<n>The findings indicate that transfer learning models, trained on large and well-structured datasets, outperform neural-based models that rely on smaller datasets.
• Score: 5.967112925028355
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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