  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.
