Abstract
As breaches get smarter and more frequent, machine learning and cybersecurity are effective defenses. The beginning emphasizes the necessity for better cyberattack protections in today's IT environment. Modern dangers are so complex that trying new methods is crucial. Machine learning, known for its self-learning, is a formidable partner in this effort. It discusses the study's objectives and the importance of comparing the new and old methods. We discuss the dataset settings, assessment metrics, ablation experiments, and experimental setup used to evaluate the recommended technique in the methods section. Trustworthy and repeatable outcomes come from well-designed experiments. A variety of cyber threat scenarios are carefully assembled to provide educators with a thorough practice environment. The recommended approach has an accuracy score of 0.85, whereas standard methods average 0.72. The recommended approach has 0.78 memory, compared to 0.65 for existing methods. The proposed approach outperforms the best method, which has an F1 score of 0.68. The strategy works since the ROC AUC value is 0.92, substantially higher than 0.78 for standard methods. This evidence often reveals that the proposed technology performs better than others, reducing phony warnings and preventing internet threats.