Machine Learning-Based Predictive Threat Intelligence Using Multi-Source Cybersecurity Data: A Synthesis of Architectures, Evidence, and Open Challenges

Abstract

The escalating volume and sophistication of cyberattacks have exposed the structural limits of signature-based and rule-driven defenses, prompting a sustained turn toward machine learning as the foundation of predictive cyber threat intelligence. This paper synthesizes recent scholarship on machine learning and deep learning approaches that fuse multiple categories of security telemetry, including network flow records, endpoint logs, open-source and dark web intelligence, identity and behavioral analytics, and vulnerability disclosure signals, into unified predictive frameworks. Rather than reporting new experimental results, the study adopts a theoretical and integrative research design, drawing on peer-reviewed literature published primarily between 2019 and 2026 to identify recurring architectural patterns, methodological trends, and persistent gaps in the field. The review finds that multi-source fusion consistently improves detection accuracy and reduces false alarm rates relative to single-source models, yet these gains are constrained by data heterogeneity, weak ground truth for forward-looking prediction tasks, adversarial manipulation of training data, and limited model interpretability. The paper further develops a conceptual framework that maps distinct categories of cybersecurity data to the machine learning techniques best suited to exploit them and to the specific analytical limitations each combination introduces. The discussion situates these findings within the broader movement toward zero trust architectures and human-in-the-loop security operations, arguing that predictive value depends less on model sophistication alone than on principled data governance, continuous validation, and sustained analyst oversight. The paper concludes by proposing directions for future empirical work, including longitudinal benchmarking and privacy-preserving federated learning across organizational boundaries.