Enhancing Cyber Security Through Predictive Analytics: Real-Time Threat Detection and Response

TL;DR

This paper demonstrates how integrating advanced predictive analytics and richer contextual features can significantly improve real-time cyber threat detection and response in Security Operations Centers.

Contribution

It introduces a framework for incorporating diverse contextual features into predictive models to enhance cyber attack detection accuracy.

Findings

Attack type influences response actions

Basic header metrics are insufficient for accurate classification

Rich contextual features improve early threat detection

Abstract

This study evaluates the application of predictive analytics for real-time cyber-attack detection and response, focusing on how statistical and machine learning methods can improve decision-making in Security Operations Centers (SOCs). Using a curated network-traffic dataset of 2,000 records, we analyzed key features such as attack type, packet length, anomaly scores, protocol usage, and geo-location patterns to assess their predictive value. Findings indicate that attack type has a measurable influence on response actions, while basic header metrics alone lack the precision needed for accurate classification. These results highlight the importance of incorporating richer contextual features - such as user behavior, asset criticality, and temporal patterns - into predictive models. By integrating such features into operational pipelines, organizations can improve early threat detection, reduce false positives, and optimize resource allocation. This research contributes actionable insights for advancing proactive, data-driven cyber defense strategies and outlines directions for future implementation in live SOC environments.