  This research paper aims to examine the applicability of predictive analytics
to improve the real-time identification and response to cyber-attacks. Today,
threats in cyberspace have evolved to a level where conventional methods of
defense are usually inadequate. This paper highlights the significance of
predictive analytics and demonstrates its potential in enhancing cyber security
frameworks. This research integrates literature on using big data analytics for
predictive analytics in cyber security, noting that such systems could
outperform conventional methods in identifying advanced cyber threats. This
review can be used as a framework for future research on predictive models and
the possibilities of implementing them into the cyber security frameworks. The
study uses quantitative research, using a dataset from Kaggle with 2000
instances of network traffic and security events. Logistic regression and
cluster analysis were used to analyze the data, with statistical tests
conducted using SPSS. The findings show that predictive analytics enhance the
vigilance of threats and response time. This paper advocates for predictive
analytics as an essential component for developing preventative cyber security
strategies, improving threat identification, and aiding decision-making
processes. The practical implications and potential real-world applications of
the findings are also discussed.
