Predictive Analytics for Cyber Threat Intelligence

EasyChair Preprint 14339

Abstract

Predictive Analytics for Cyber Threat Intelligence (CTI) is an emerging field that leverages advanced data analysis techniques to anticipate and mitigate potential cyber threats. By employing machine learning algorithms, statistical models, and big data analytics, predictive analytics aims to enhance the accuracy and timeliness of threat detection and response. This approach involves analyzing historical threat data, identifying patterns and anomalies, and generating forecasts about future threat activities. Predictive models can help organizations anticipate new attack vectors, understand adversary behaviors, and prioritize defensive measures effectively. The integration of predictive analytics into CTI frameworks promises to revolutionize cybersecurity strategies by shifting from reactive to proactive defense mechanisms, ultimately leading to more resilient and adaptive cybersecurity infrastructures. This paper explores the methodologies, benefits, and challenges associated with predictive analytics in CTI, providing insights into its potential to transform the landscape of cybersecurity.

Keyphrases: Cyber Security, learning, machine

@booklet{EasyChair:14339,
  author    = {Obaloluwa Ogundairo and Peter Broklyn},
  title     = {Predictive Analytics for Cyber Threat Intelligence},
  howpublished = {EasyChair Preprint 14339},
  year      = {EasyChair, 2024}}