Runtime Efficiency Comparison Across IDS Models

Ali Alqazzaz published a dataset on figshare in April 2026. The dataset likely contains performance metrics for intrusion detection system models, specifically focusing on runtime efficiency. It is associated with research on MedDefender-MHAN, an explainable multi-head attention network for healthcare IoT threat detection.

Use Cases

• Benchmarking model inference latency based on reported sub-3ms performance
• Comparing detection accuracy across models based on reported 99.47% and 98.92% figures
• Evaluating throughput of security models based on the 435 samples per second metric
• Analyzing trade-offs between accuracy and runtime efficiency in intrusion detection systems

Strengths

• Performance metrics include specific numbers: detection accuracies of 99.47% and 98.92%
• Runtime metrics include specific numbers: sub-3ms inference latency and throughput of 435 samples per second
• Dataset is associated with evaluation on benchmark datasets CICIDS2017 and TON_IoT

Limitations

• Dataset size is only 5.5 KB, indicating a very limited scope
• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download

Provenance

Source

figshare

Freshness

Last updated 2026-04-17 17:39:37; freshness should be verified