融合神经符号与迁移学习，提升网络入侵检测精度。
Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
- 结合迁移学习与不确定性量化，构建新型神经符号框架
- 在大规模数据集上训练的模型显著优于小数据模型
- 适合安全研究者与工业界部署智能检测系统
迁移学习在计算机视觉、自然语言处理和医学影像等领域广泛应用，因其出色的子任务处理能力和跨数据集适应性。然而，其在网络安全领域的应用尚未充分探索。本文提出一种创新的神经符号人工智能框架，用于网络入侵检测系统，该系统在对抗网络恶意行为中至关重要。框架融合迁移学习与不确定性量化技术。实验表明，在大规模结构化数据集上训练的迁移学习模型，性能显著优于依赖小规模数据的纯神经网络模型，为网络安全解决方案开启新纪元。
原文摘要 · Abstract (English)
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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