Enhancing cybersecurity risk assessment using temporal knowledge graph-based explainable decision support system

Abstract

Assessing cybersecurity policies is crucial for any organization to combat evolving cyber threats. The absence of a comprehensive dataset has prevented previous studies from analyzing the risk of organizations’ cybersecurity policies. Past studies have not considered temporal information in the policies. Analysis of cybersecurity policies using attention mechanism requires automated determination of optimal number of attention units which remains unaddressed. Moreover, absence of interpretation in cybersecurity studies creates a barrier to understanding policy vulnerabilities and developing targeted solutions. To address these challenges, we develop a decision support system which (i) enhances risk classification of organization’s cybersecurity policies, (ii) develops a comprehensive cybersecurity policy dataset from the websites of 190 companies, transformed into a knowledge graph to capture entity relationships among various policies, (iii) integrates temporal information into the knowledge graph by incorporating time stamps from event sequences in cyberattack information, (iv) develops Explainable Factor Analysis based Multi-Head Attention mechanism, which automates the determination of the optimal number of attention units and optimizes data allocation across attention units using factor analysis, and (v) utilizes attention heatmaps and shapley values for interpretability. Our cybersecurity policy dataset is used as a case study with four benchmark datasets for further validation. Results reveal that our model outperforms the other state-of-the-art, achieving an 87.78% $F_1$ score, followed by robustness checking and statistical significance testing. Finally, Shapley values are used to interpret the model’s output to identify vulnerabilities within the organizational policies, providing crucial insights enabling decision-makers to enhance their cybersecurity policies and mitigate potential threats.