A Comprehensive Predictive Information Security Risk Intelligence Framework for IT Service Environments: An Empirical Machine Learning Approach

Yogish Pai U; Dr. Suresha D

doi:10.29284/fgdqvf59

Authors

Yogish Pai U Research Scholar, Institute of Computer Science and Information Science, Srinivas University, Mangalore, Karnataka, India
Dr. Suresha D Professor, Computer Science and Engineering., Srinivas Institute of Technology, College of Computer Science and Information Science, Srinivas University, Mangalore, India

DOI:

https://doi.org/10.29284/fgdqvf59

Keywords:

Cybersecurity, Machine Learning, Predictive Analytics, Threat Intelligence, Behavioral Analytics, IT Services

Abstract

The escalating sophistication of cyber threats in IT service environments necessitates a paradigm shift from reactive to predictive security frameworks. This paper presents PISRI (Predictive Information Security Risk Intelligence), a novel machine learning-driven framework that integrates behavioral analytics, real-time threat intelligence, and adaptive risk assessment for proactive threat mitigation. The framework employs an ensemble of Isolation Forest, LSTM networks, and gradient boosting algorithms to analyze multi-dimensional security data streams in real-time. We conducted a comprehensive evaluation using established public cybersecurity datasets, including CICIDS2017 (2.83M records), UNSW-NB15 (2.54M records), and NSL-KDD (148K records), representing diverse attack scenarios and network environments. Statistical analysis demonstrates significant improvements over baseline systems: 16.3% increase in threat detection precision (p < 0.001), 28.7% reduction in mean time to detection, and 34.2% improvement in false positive reduction. The framework achieved an F1-score of 0.834 and AUC-ROC of 0.912 while maintaining scalable performance up to 15,000 events per second. Results validate the effectiveness of predictive intelligence approaches in enhancing cybersecurity posture for IT service environments. The framework's modular architecture and open-source implementation facilitate adoption across diverse organizational contexts.

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