Big Data and Cybersecurity: Using Analytics to Predict and Prevent Cyber Threats

Dinesh Kumar Budagam, Naresh Kumar Miryala

Abstract: In the period of digital transformation, Cyber threats have become more difficult, which simplifies data of easy and the defenses are more necessary in active state. This study proposes a novel ensemble-based analytical framework using Random Forest (RF) combined with Bayesian Inference for predicting and preventing cyber threats. The approach leverages big data analytics to process massive volumes of real-time network logs, system behavior data and user activity patterns to identify anomalies and potential breaches. Through detailed analysis, the model demonstrates high detection accuracy and low false positive rates by effectively capturing non-linear threat patterns and incorporating probabilistic reasoning. Interpretation of the results shows that integrating with probabilistic models enhances prediction reliability across varied threat scenarios, including malware propagation and insider attacks. The model's robustness and effectiveness are validated by the comparative results on standard benchmark datasets, which demonstrate an accuracy of 96.7% and specificity of 97%.

Keywords: Cybersecurity, Big Data, Bayesian Inference, Random Forest