Authors : Su Mon Tint Soe; Dr. Cho Me Me Maung

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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DOI : https://doi.org/10.38124/ijisrt/26sep1445

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : In the contemporary landscape of increasingly sophisticated network attacks, effectively selecting salient features from network traffic data has become a critical step in developing robust Intrusion Detection Systems (IDS). However, the efficacy of Machine Learning (ML) and Deep Learning (DL)-based IDS is often compromised by the high dimensionality of network traffic features, disparate data distributions, and the presence of irrelevant features.

Keywords : Network Traffic Analysis, CSE-CIC-IDS2018, Feature Selection, Machine Learning, Correlation Analysis, Deep Learning, Intrusion Detection System.

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