aspg-3-3092-1791419432 20261008003032 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 17 1 Amrita Bhatnagar Shobhit Institute of Eng. & technology, Meerut, India Arun Giri Shobhit Institute of Eng. & technology, Meerut, India Aditi Sharma Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, India A network Intrusion detection system is a system that can find out different types of attacks. ANIDS is used to find out the noble type of attack by using machine learning and deep learning techniques. These techniques are very useful to find out those attacks whose patterns are not stored in the database. Therefore, these types of systems need more research to improve their accuracy and reduce the false alarm rate. In this paper, we are going to propose an ensemble framework for NIDS using different ML and DL techniques. In this paper, we have used the XGBOOST algorithm for feature extraction and for classification, CNN and RNN deep learning techniques are used. This ensemble model is used for the binary and multiclassification of attacks. Our model was checked on the dataset CICIDS-2018 which gives a better accuracy and low false alarm rate. 2025 67 77 3092 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.170105 https://www.americaspg.com/journal/3/article/3092 https://www.americaspg.com/storage/31736182620.pdf