Cyber Threat Predictive Analysis for Improving Cyber Supply Chain Security

Abstract

The complexity and interconnectivity of modern cyber supply chains make them increasingly susceptible to sophisticated and evolving cyber threats. As organizations depend on a diverse network of suppliers, partners, and service providers, even a single weak link can compromise the entire ecosystem. Traditional deep learning models like Artificial Neural Networks (ANN), while powerful, often face challenges in terms of limited interpretability, higher computational requirements, and reduced adaptability when dealing with dynamic and heterogeneous supply chain environments.To address these limitations, this project proposes a hybrid machine learning approach that leverages the strengths of multiple classical algorithms—including Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Stochastic Gradient Descent (SGD). Each of these algorithms brings unique advantages: for instance, Naive Bayes offers simplicity and efficiency, SVM provides strong performance in high-dimensional spaces, while ensemble methods like Random Forest enhance robustness and accuracy.By combining these models either through ensemble techniques or comparative analysis, the system aims to achieve faster and more scalable predictions without sacrificing transparency. This hybrid strategy not only enhances interpretability, allowing security analysts to better understand and trust the model outputs, but also supports real-time adaptation to new and emerging cyber threats. Ultimately, this approach is designed to improve the resilience and security posture of supply chains by proactively identifying vulnerabilities and mitigating risks before they escalate into significant breaches.In doing so, the proposed system empowers organizations to navigate the increasingly complex landscape of cyber risk management with greater confidence, agility, and insight—safeguarding critical infrastructure and ensuring business continuity in a rapidly changing threat environment.