Unified NIDS Dataset Advances ML Detection and Synthetic Data Generation
Researchers have created a unified multi-modal network intrusion detection dataset by consolidating four major cybersecurity benchmarks (CIC-IDS-2017, CIC-IoT-2023, UNSW-NB15, and CIC-DDoS-2019) into a single feature space that includes flow-level data, packet payloads, and temporal context. The work evaluates both traditional machine learning classifiers for attack detection and adversarial learning methods for generating synthetic network traffic data, using statistical rigor to measure synthetic data quality across fidelity, utility, and privacy dimensions. The findings demonstrate stable ML models for intrusion detection alongside generative models that produce high-quality synthetic data, combining the Synthetic Data Vault framework with statistical tests and divergence measures for validation.