Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
This addresses cybersecurity intrusion detection, but appears incremental as it applies known transfer learning techniques to a less-explored domain.
The paper tackles network intrusion detection by developing a neurosymbolic AI framework with transfer learning and uncertainty quantification, finding that transfer learning models trained on large datasets outperform neural-based models on smaller datasets.
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.