Machine Learning-Enhanced Simulation of Multi-Vector Email Malware Spread in Organizational Networks

Abstract

The proliferation of sophisticated email-borne malware necessitates advanced modeling techniques to predict and mitigate cyber threats. While prior research established foundational lattice-based models for virus propagation via email, contemporary attacks exploit multi-vector infiltration (e.g., malicious links, macros, and embedded scripts) and evade traditional detection. This paper presents a novel hybrid model combining agent-based deterministic simulations with machine learning-driven defense adaptations to quantify malware spread in heterogeneous organizational networks. Key innovations include: (1) a dynamic network topology incorporating hierarchical user roles and device diversity (desktop), (2) probabilistic infection pathways aligned with real-world phishing metrics (Verizon DBIR), and (3) an adaptive detection module trained on behavioral anomalies i.e, email burst rates, attachment types. Simulations demonstrate a 40–62% improvement in outbreak containment compared to classical models, with false positives reduced by 28% through ML-augmented filtering. The framework bridges theoretical epidemiology and practical cybersecurity, offering actionable insights for IT policy design.