Scalability Metrics Across Dimensions for a Quantum-Safe ML-Assisted Security System

Experimental results from 2026 evaluate an integrated Intent-Based Networking security system. The dataset, authored by Kumar Sekhar Roy and shared under CC-BY-4.0, contains metrics for cryptographic correctness, access control performance, anomaly detection capability, and scalability under increasing workloads. It is a small dataset of 5.5 KB.

Use Cases

• Benchmarking post-quantum signature generation and verification performance based on reported 100% success rate.
• Evaluating anomaly detection model efficacy based on the use of Isolation Forest and MiniBatch KMeans algorithms.
• Analyzing scalability of log-processing throughput under increasing event loads as described in the evaluation.
• Assessing trade-offs between security enforcement and system usability based on the integrated framework's findings.

Strengths

• Experimental results demonstrate 100% success in post-quantum signature generation and verification.
• Anomaly detection reported no observed false negatives in the evaluated scenarios.
• Log-processing throughput remained stable as the number of events grew.
• Evaluation was conducted using a controlled prototype testbed with reproducible experimental settings.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description identifies limitations such as certificate expiry handling gaps and lack of large-scale statistical validation.

Provenance

Source

figshare

Collection Method

Generated from a controlled prototype testbed evaluating a security system.

Freshness

Last updated 2026-05-15 17:37:03; freshness should be verified.