Healthcare IoT Challenges and PQ-CZTA Solutions: Intrusion Detection Performance Data

Hashim Hussain published this dataset on 2026-05-28. It contains evaluation results for a proposed Post-Quantum Cognitive Zero-Trust Architecture (PQ-CZTA) for securing healthcare IoT systems. The data likely includes performance metrics from testing the framework on six intrusion detection datasets, including NSL-KDD, CIC-IDS2017, and MedBIoT.

Use Cases

• Benchmarking intrusion detection systems based on F1-scores reported across multiple IoT datasets.
• Evaluating post-quantum cryptography latency based on reported handshake times of 3.1–4.4 seconds.
• Studying the impact of SMOTE oversampling on model performance based on the reported 5–20% F1-score contribution.
• Analyzing adaptive zero-trust policy decisions based on dynamic trust scores derived from ML classifiers.

Strengths

• Performance metrics are reported with specific F1-scores ranging from 0.972 to 1.000.
• The framework was evaluated on six named, diverse intrusion detection datasets, including modern IoT traffic sets.
• An ablation study quantifies the contribution of individual components like SMOTE oversampling.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 5.5 KB, indicating limited scope, likely containing only summary results.

Provenance

Source

Hashim Hussain via figshare.

Collection Method

Results from evaluating a proposed security architecture (PQ-CZTA) on six public intrusion detection datasets.

Freshness

Last updated 2026-05-28 17:42:30; freshness should be verified.