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Conference proceeding
Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
MILCOM IEEE Military Communications Conference, pp.496-501
IEEE Military Communications Conference
IEEE Military Communications Conference (MILCOM) (Los Angeles, California, USA, 10/06/2025–10/10/2025)
10/2025
Web of Science ID: WOS:001708627800081
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 sub-tasks 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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Details
- Title
- Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
- Publication Details
- MILCOM IEEE Military Communications Conference, pp.496-501
- Resource Type
- Conference proceeding
- Conference
- IEEE Military Communications Conference (MILCOM) (Los Angeles, California, USA, 10/06/2025–10/10/2025)
- Publisher
- IEEE
- Series
- IEEE Military Communications Conference
- Number of pages
- 6
- Grant note
- U.S. Military Academy (USMA): W911NF-23-2-0108 Defense Advanced Research Projects Agency (DARPA): USMA 23004
This work was supported by the U.S. Military Academy (USMA) under Cooperative Agreement No. W911NF-23-2-0108 and the Defense Advanced Research Projects Agency (DARPA) under Support Agreement No. USMA 23004. The views and conclusions expressed in this paper are those of the authors and do not reflect the official policy or position of the U.S. Military Academy, U.S. Army, U.S. Department of Defense, or U.S. Government.
- Copyright
- © 2025, IEEE
- Identifiers
- WOS:001708627800081; 99381587050406600
- Academic Unit
- Mathematics and Statistics; Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English