Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration

The perpetual evolution of cyberattacks, especially in the realm of Internet of Things (IoT) networks, necessitates advanced, adaptive, and intelligent defence mechanisms. The integration of expert knowledge can drastically enhance the efficacy of IoT network attack detection systems by enabling them to leverage domain-specific insights. This paper introduces a novel approach by applying Neurosymbolic Learning within the Explainable Artificial Intelligence (XAI) framework to enhance the detection of IoT network attacks while ensuring interpretability and transparency in decision-making. Neurosymbolic Learning synergizes symbolic AI, which excels in handling structured knowledge and providing explainability, with neural networks, known for their prowess in learning from data. Our proposed model utilizes expert knowledge in the form of rules and heuristics, integrating them into a learning mechanism to enhance its predictive capabilities and facilitate the incorporation of domain-specific insights into the learning process. The XAI framework is deployed to ensure that the predictive model is not a “black box”, providing clear, understandable explanations for its predictions, thereby augmenting trust and facilitating further enhancement by domain experts. Through rigorous evaluation against benchmark IoT network attack datasets, our model demonstrates superior detection performance compared to prevailing models, along with enhanced explainability and the successful incorporation of expert knowledge into the adaptive learning process. The proposed approach not only fortifies the security mechanisms against network attacks in IoT environments but also ensures that the knowledge discovery and decision-making processes are transparent, interpretable, and verifiable by human experts.