Cyber Attack Intelligence Through Statistical Methods: Trend Identification and Predictive Forecasting

In the evolving landscape of cybersecurity, understanding the patterns and frequency of cyber attacks is essential for effective threat mitigation and future preparedness. This research presents a comprehensive statistical analysis of cybersecurity threat data to identify the most prevalent types of attacks. Using real-world datasets, the study employs descriptive and inferential statistical methods to uncover trends and relationships among various attack vectors. Visualizations such as heatmaps, time series plots, and correlation matrices are developed to provide intuitive insights into threat behavior over time. Furthermore, predictive modeling techniques, including regression analysis and time series forecasting, are utilized to estimate the likelihood and intensity of future cyber attacks. The results offer valuable insights for security professionals and organizations aiming to strengthen their defensive strategies and proactively address emerging threats.