Humayun, ZayedMajumder, RiddhaQaiyum, Md.TanzimIshrak, FathinNeha, Mehrin Amin2026-01-122026-01-1220252025-10ID 23341093ID 24241049ID 20301027ID 21201070http://hdl.handle.net/10361/27426Cataloged from PDF version of thesis.Includes bibliographical references (pages 39-43).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Network intrusion detection systems face two critical challenges: the need to identify specific attack types beyond binary classification and the ability to adapt to evolving threats while maintaining low false positive rates. In response, we propose a hierarchical multi-level Poisson Variational Autoencoder (PVAE) system augmented with reinforcement learning-based ensemble weighting for intrusion detection. At its core, a three-level PVAE chain with normalizing flows captures network traffic patterns at packet, flow and session levels. In our research, we applied class-conditional radial recalibration fitted on validation data to align the latent space separately for each attack class. Rather than training multiple separate models, we create ensemble diversity by instantiating three architectural variants from a single trained checkpoints. A Proximal Policy Optimization (PPO) agent then learns dynamic persample weights based on prediction confidence and other patterns. Finally, an XGBoost meta-learner refines the PPO-weighted ensemble outputs using uncertainty diagnostics to produce the final classification. Evaluated on the NF-UQ-NIDSv2 dataset with 14 traffic classes, our system achieves 89.17% multiclass accuracy with 88.01% weighted F1-score, while maintaining strong binary discrimination with 98.11% AUROC and 99.11% PR-AUC for normal versus attack detection.53 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Intrusion detectionNetwork intrusion detection systemsPoisson variational autoencoderNetwork securityGenerative modelsReinforcement learning.Computer networks--Security measures.Intrusion detection systems (Computer security).Computer security.Thesis