PDNet-IDS26: Parkinson's Disease Features for Intrusion Detection

A dataset combining Parkinson's disease features with network characteristics for intrusion detection system research. The dataset was sourced from Kaggle, but the author, organization, and creation date are unknown. The specific number of records and features is not provided in the available metadata.

Use Cases

• Train machine learning models for network intrusion detection based on derived network features.
• Research correlations between biomedical signals and network traffic patterns for anomaly detection.
• Benchmark cybersecurity algorithms using a dataset that incorporates health-related feature engineering.

Strengths

• The dataset integrates two distinct domains: biomedical (Parkinson's Disease) and cybersecurity (Intrusion Detection).
• The title suggests a specific, applied research focus for the year 2026.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle