The prevailing approaches in Network Intrusion Detection Systems (NIDS) are
often hampered by issues such as high resource consumption, significant
computational demands, and poor interpretability. Furthermore, these systems
generally struggle to identify novel, rapidly changing cyber threats. This
paper delves into the potential of incorporating Neurosymbolic Artificial
Intelligence (NSAI) into NIDS, combining deep learning's data-driven strengths
with symbolic AI's logical reasoning to tackle the dynamic challenges in
cybersecurity, which also includes detailed NSAI techniques introduction for
cyber professionals to explore the potential strengths of NSAI in NIDS. The
inclusion of NSAI in NIDS marks potential advancements in both the detection
and interpretation of intricate network threats, benefiting from the robust
pattern recognition of neural networks and the interpretive prowess of symbolic
reasoning. By analyzing network traffic data types and machine learning
architectures, we illustrate NSAI's distinctive capability to offer more
profound insights into network behavior, thereby improving both detection
performance and the adaptability of the system. This merging of technologies
not only enhances the functionality of traditional NIDS but also sets the stage
for future developments in building more resilient, interpretable, and dynamic
defense mechanisms against advanced cyber threats. The continued progress in
this area is poised to transform NIDS into a system that is both responsive to
known threats and anticipatory of emerging, unseen ones.

融合神经符号人工智能（NSAI）和传统网络入侵检测系统（NIDS）的研究，通过分析网络流量数据类型和机器学习架构，展示了 NSAI 在提供对网络行为更深入洞察力方面的独特能力，从而改善了检测性能和系统的适应性。这种技术的融合不仅增强了传统 NIDS 的功能，也为构建更具弹性、可解释性和动态防御机制以应对先进网络威胁的未来发展奠定了基础。