Autoencoder-based IoT IDS trained on normal traffic and evaluated on unseen attacks (zero-day) using the TON_IoT dataset.
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Updated
Oct 30, 2026 - Python
Autoencoder-based IoT IDS trained on normal traffic and evaluated on unseen attacks (zero-day) using the TON_IoT dataset.
Hybrid Certainty Factor-XGBoost approach for cyberattack detection on TON_IoT dataset
KNN hyperparameter study for multi-class IoT intrusion detection on the TON_IoT network dataset (scikit-learn, ADASYN, PCA).
Real-time IoT cyber-attack detection using a custom WebSocket streaming pipeline and an incrementally-trained ML ensemble on the TON_IoT datasets
Explainable hybrid IDS with leakage-aware cross-dataset evaluation on UNSW-NB15, CIC-IDS2017, and ToN-IoT.
Reproduction of Shaikhanova et al. (MDPI Sensors, 2025) — ML-based IoT intrusion detection using TON_IoT dataset with RF, LightGBM, XGBoost & Logistic Regression.
Constraint-aware adversarial-robust NIDS on CIC-IoT2023 and TON_IoT
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