PDNet-IDS26: A Healthcare 5.0 Multiclass Intrusion Detection Dataset with Biomedical and Network Features - Lui, Pedro H.; Siqueira, Lucas P.; Kazienko, Juliano F.; Quincozes, Silvio E.; Quincozes, Vagner E.; Welfer, Daniel; Fideles, Douglas Rodrigues; Kreutz, Diego Luis (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21576, 2026)
Intrusion Detection Systems (IDS) are central to securing Healthcare 5.0 environments, yet existing IDS datasets rely on network features only and exclude patient clinical data. This gap is critical in telemonitoring, where evolving physiological signals define patient-specific baselines that could enhance context-aware intrusion detection. This work introduces the Parkinson’s Disease with Network Features – Intrusion Detection Systems 2026 (PDNet-IDS26), a multiclass dataset that merges network traffic with Parkinson’s Disease (PD) telemonitoring records. We integrated literature-derived physiological features into network captures to enable unified intrusion detection. Results involving the merged dataset indicate that Replay and False Data Injection attacks (FDI) overcome other scenarios, achieving average F1-scores of 93% and 77%, respectively. Explainability results show that the network (Length) and biomedical (Age) features are critical for identifying FDI attacks.