Paper Title
A Review on Network Intrusion Detection System Using Hybrid Boosting Machine Learning Technique

Abstract
The hybrid model adds the strengths of multiple boosting techniques, including AdaBoost and Gradient Boosting, to create a more strong classification method for analysing network traffic by going through these models, they can differentiate between benign and unauthorized activities in real-time, thus enhancing the execution of intrusion detection. The dataset is crucial for training and testing, It includes real-world network traffic, with both different scenarios. Preliminary findings suggest that the hybrid method is better than old machine learning methods and models, providing less false positives and an improved true positive rate in detecting multiple attacks,for example DDoS, port scans, and malware communications. The hybrid boosting model is optimized using hyperparameter adjustment and cross- validation. [1] Keywords - Hybrid Boosting, Random Forest, Xg Boost , Ensemble Model.