A FRACTIONAL-ORDER PREDICTIVE MODEL FOR CYBER-RISK IN IT ASSET DISPOSAL (ITAD) SYSTEMS

Abstract

The secure disposal of IT assets has become an essential element of modern cybersecurity, largely due to the rapid increase in obsolete digital equipment that still contains sensitive or confidential information. When these devices are not handled properly at end-of-life, organizations face significant exposure to data breaches, identity theft, regulatory non-compliance, and reputational harm. In this work, a new fractional-order predictive model is introduced to evaluate cyber-risk within IT Asset Disposal (ITAD) processes. The model incorporates memory-driven behavior to reflect how risk accumulates and persists throughout the data lifecycle. It examines four major ITAD stages—data at rest, data in motion, data in use, and data destruction—while integrating compliance factors derived from NIST 800-88, ISO 9001, ISO 14001, ISO 45001, and R2v3 requirements. Using Caputo fractional derivatives, a nonlinear system is developed to describe how cyber-risk grows, decreases, and transfers between stages under different operational conditions. Simulation results show that fractional-order dynamics provide a more realistic representation of cyber-risk trends than traditional integer-order models, especially in environments where vulnerabilities linger or remediation is delayed. Overall, the proposed framework offers a mathematically robust foundation for building secure, compliant, and operationally effective ITAD programs, delivering practical value for enterprises, recyclers, auditors, and regulatory authorities.