REAL TIME CYBERSECURITY THREAT ASSESSMENT DYNAMIC RISK SCORING WITH HYBRID DATA SCIENCE MODELS

Abstract

As the world of cybersecurity continues to grow increasingly complex due to the constant advancement and introduction of new technologies, real-time threat analysis is necessary to stand up against the threats. Traditional frameworks are not very effective when assessing risks because they fail to account for the constant evolution of cyber threats. This paper presents a novel approach that combines machine learning, statistical analysis, and threat intelligence to produce real-time cybersecurity risk scores. The model uses anomaly detection, predictive analysis, and adaptive learning to examine risks and probable data intrusion. Integrating structured and unstructured data sources in the proposed approach improves threat detection, minimizes false alarms, and optimizes response times. The results from empirical studies show that the hybrid model is more accurate and less rigid than the traditional approaches. The results compiled in this study prove that it is crucial to use sophisticated data science methods to predict cybersecurity threats and prevent cyberattacks.