Cybersecurity Incident Prediction Using Time Series Analysis and Machine Learning

Abstract

The increasing frequency and sophistication of cybersecurity incidents necessitate proactive approaches to predict and mitigate potential breaches. This paper investigates the integration of time series analysis techniques with machine learning models to forecast cybersecurity incidents. Time series analysis allows for the identification of temporal patterns and trends in historical incident data, while machine learning models enhance predictive accuracy by leveraging these insights. The study presents a comprehensive framework that encompasses data preprocessing, feature selection, model training, and evaluation. Various machine learning algorithms, including decision trees, support vector machines, and neural networks, are employed to assess their effectiveness in predicting incidents. Results demonstrate that combining time series analysis with machine learning significantly enhances the ability to anticipate cybersecurity threats, providing organizations with valuable insights for implementing preemptive measures. The findings contribute to the ongoing discourse on advancing cybersecurity strategies and highlight the potential of predictive analytics in safeguarding digital assets.