Enabling Efficient and Interpretable Cybersecurity Reasoning Through Hyperdimensional Computing

Abstract

Knowledge graphs play a crucial role in addressing the complexities of cybersecurity, as the increasing frequency and sophistication of cyber threats pose significant challenges to traditional defense technologies. In this article, we propose a novel reasoning model, called INCYSER, that is tailored for cybersecurity. By leveraging hyperdimensional computing (HDC) as a symbolic and transparent computational model, INCYSER offers efficient and interpretable reasoning capabilities, ensuring reliable and trustworthy outcomes. Our model combines embedding-based unsupervised learning and HDC-based graph representation learning to construct a general representation for cybersecurity knowledge graphs, enabling diverse tasks including reasoning and general graph operations. Experimental evaluations demonstrate the effectiveness and efficiency of INCYSER, surpassing state-of-the-art models in link prediction and triple classification tasks. Additionally, a comprehensive ablation study examines the impact of various hyperparameters, showcasing the versatility of INCYSER. This work contributes to advancing the field of cybersecurity by introducing an interpretable and representation-based reasoning model for cybersecurity knowledge graphs.