Predictive Cyber Security Framework Using Machine Learning and Threat Intelligence

Abstract

State-of-the-art security systems are often unable to counter modern threats effectively, since many of them are still based on a signature-based approach. Therefore, they fail to detect zero-day attacks and multi-stage Advanced Persistent Threats (APTs). While individual components - log-based ML classification, NLP-driven dark-web analysis, CTI enrichment - have each been explored separately, no existing framework integrates all three within a unified, CTI-fused architecture explicitly oriented toward reducing SOC alert volume. Additionally, the paper identifies lab-to-production drift as a critical design consideration for future validation. This paper presents a concept for a five-layer-based predictive security approach that, in the early stages of a potential attack, aims to detect and prevent it by using structured and unstructured internal and external data. The core layer for the estimation of threat probabilities is a log layer, where, by means of a Random Forest algorithm, structured log data, as well as communication records, are processed in order to compute a threat probability. In addition to the log layer, there is a text layer where, using LSTM/Transformer, unstructured textual reports from the dark web and open-source intelligence (OSINT) are processed. A fusion layer combines these outputs with Cyber Threat Intelligence (CTI), linking indicators to known malware campaigns. The framework aims to improve SOC efficiency by prioritizing high-confidence, context-rich alerts over raw volume. The paper also identifies model validation and data-drift awareness as essential considerations when transferring laboratory-trained models into real systems, with concrete mitigation mechanisms proposed as future work. However, an empirical validation as well as an implementation of the presented concepts is still to be done in the future.