This research presents an in-depth exploration of the FI-LDA model, showcasing its efficacy in anticipating and preventing intrusions, thereby bolstering security measures within cloud environments. The study introduces a novel approach to intrusion prevention, fostering a robust predictive model that significantly enhances the system's capability to discern evolving attack patterns. Leveraging fuzzy modeling, the research demonstrates the utilization of vast amounts of unlabeled data, resulting in heightened accuracy and reliability of the system. The evaluation of diverse elements crucial for cybersecurity underscores the comprehensive approach adopted to achieve the research objectives. While the FI-LDA model exhibited a favorable trade-off, addressing a pervasive flaw, there remains a call for further refinement to detect assault patterns more effectively. The research concludes by highlighting the commendable effectiveness of the FI-LDA model in identifying and detecting malicious activities within the cloud environment, affirming its strong overall performance and contribution to advancing intrusion detection systems.