Paper Key : IRJ************255
Author: Prashant Singh,Ram Krishna Paramhans Dubey
Date Published: 04 Jan 2025
Abstract
Cybersecurity breaches continue to pose significant threats to organizations, with ever-evolving attack vectors and increasing sophistication of cybercriminals. Forecasting cyber attacks has emerged as a crucial field of study, leveraging predictive models to anticipate and mitigate potential threats. This paper provides an in-depth analysis of state-of-the-art predictive models used for cybersecurity breach forecasting. We evaluate machine learning techniques, statistical models, and hybrid approaches in their ability to predict cyber threats based on historical data, real-time monitoring, and behavioral analytics. Furthermore, the study highlights the role of feature engineering, dataset quality, and anomaly detection in enhancing prediction accuracy. By identifying gaps and limitations in current methodologies, we propose a roadmap for future research and development, emphasizing the integration of advanced algorithms, such as deep learning and reinforcement learning, with real-time threat intelligence. This comprehensive review aims to equip cybersecurity professionals and researchers with actionable insights to bolster proactive defense mechanisms against cyber attacks.