A unified multi-modal NIDS dataset from CIC-IDS-2017, CIC-IoT-2023, UNSW-NB15 and CIC-DDoS-2019 is used to train stable ML attack classifiers and to generate synthetic data whose fidelity and utility are assessed via SDV, f-divergences, TRTS/TSTR tests and non-parametric statistics.
Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare
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Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation
A unified multi-modal NIDS dataset from CIC-IDS-2017, CIC-IoT-2023, UNSW-NB15 and CIC-DDoS-2019 is used to train stable ML attack classifiers and to generate synthetic data whose fidelity and utility are assessed via SDV, f-divergences, TRTS/TSTR tests and non-parametric statistics.