AI-Driven Automated Threat Hunting with Predictive Analytics

Rajashekhar Reddy Kethireddy

doi:10.70589/JRTCSE.2022.1.3

Authors

Rajashekhar Reddy Kethireddy Department Of Software Engineering, IBM Author

DOI:

https://doi.org/10.70589/JRTCSE.2022.1.3

Keywords:

AI, Automated Threat Hunting, Predictive Analytics, Cybersecurity, Machine Learning

Abstract

The traditional methods of threat detection are fast proving to be inadequate in view of increasingly sophisticated and persistent cyber-attacks. This paper discusses automated threat hunting processes, integrated with Artificial Intelligence and predictive analytics that could further improve the identification, prediction, and mitigation of potential security breaches. A proposed framework, therefore, applies machine learning algorithms in tandem with data-driven models to analyze high volumes of network data in near real time, detect patterns of anomalies, and predict emerging threats with a high degree of accuracy. The study further ascertains the appropriateness of various AI techniques, including deep learning and natural language processing for threat intelligence and response times. Besides, data quality issues, model interpretability, and requirements to learn continuously in order to adapt to the dynamic threat landscape are considered. The results obtained show that AI-driven automated threat hunting reduces the time and resources needed for threat detection while increasing the precision of identifying malicious activities. Not only does this approach harden the security posture of an organization, but it will also offer a scalable solution that could meet demanding complex network environments.

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