Hossain, Muhammad IqbalHumayun, ZayedBishal, M. Ridhwan GaniYeasin, Tasin MohammadFuad, Mohammad Salah AkramRizvee, RaidaMueed, Neamul2026-01-082026-01-0820252025-10ID 21201529ID 21201090ID 21201361ID 21241032ID 21201750http://hdl.handle.net/10361/27413Cataloged from PDF version of thesis.Includes bibliographical references (pages 41-42).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.Intrusion detection systems (IDS) are constantly evolving in the field of network security to safeguard critical data assets against a growing array of sophisticated cyber threats, such as malevolent botnets, massive Distributed Denial of Service (DDoS) attacks, slow-rate DDoS attacks, advanced persistent threats (APTs), and zero-day exploits. Moreover, any organization’s network infrastructure remains vulnerable to different types of attacks, such as system abuse, security lapses, and break-ins. The Network Intrusion Detection System (NIDS) used in a network identifies such penetration attempts and intrusions. Researchers using deep learning (DL) have proposed increasingly capable IDS to protect critical networks; however, IDS are difficult to deploy in such environments because of high false-alarm rates (FAR). In this paper, we propose a hybrid framework that combines conditional variational autoencoder (CVAE)–based synthetic data generation with a Bayesian VAE model to reduce false-alarm rates in multi-class intrusion detection. This approach aims to lower FAR while maintaining strong detection performance by augmenting minority classes with class-consistent synthetic samples and leveraging calibrated Bayesian decisions.54 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 detection systemsNetwork securityBayesian variational autoencoderFalse alarm rateSynthetic data generationConditional variational autoencoderComputer networks--Security measures.Computer security.Electronic data processing.Data mining.Thesis