Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy
This addresses the need for explainable and accurate AI in high-stakes domains like cybersecurity and privacy, though it appears incremental as it builds on existing neuro-symbolic approaches.
The paper tackles the problem of AI systems lacking explainability and safety, especially in cybersecurity and privacy, by proposing neuro-symbolic AI to combine neural networks and knowledge graphs for enhanced reasoning and generalization.
Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to enhance explainability and safety in AI systems. This approach addresses a key criticism of current generation systems, namely their inability to generate human-understandable explanations for their outcomes and ensure safe behaviors, especially in scenarios with \textit{unknown unknowns} (e.g. cybersecurity, privacy). The integration of neural networks, which excel at exploring complex data spaces, and symbolic knowledge graphs, which represent domain knowledge, allows AI systems to reason, learn, and generalize in a manner understandable to experts. This article describes how applications in cybersecurity and privacy, two most demanding domains in terms of the need for AI to be explainable while being highly accurate in complex environments, can benefit from Neuro-Symbolic AI.