Synthetic Incident Reports

This synthetic dataset, Synthetic Incident Reports Dataset, has been created solely for academic research and evaluation purposes within the project Zero-Knowledge Proof Framework for Secure Cyber Incident Reporting.

The dataset is the intellectual property of the authors and is distributed under a non-exclusive license for research, academic, and non-commercial use only. Any redistribution, modification, or commercial use requires prior written permission from the authors.

All rights reserved.

Purpose:This dataset was generated to simulate real-world cyber incident reporting scenarios in order to evaluate the performance of the proposed Zero-Knowledge Proof Framework. It provides a controlled, privacy-preserving benchmark for assessing latency, throughput, scalability, and resource efficiency under burst-load conditions.

Composition:

Number of Records: Configurable between 1,000–10,000 daily reports for experiments.

Format: Comma-Separated Values (CSV).

Fields (example structure):

report_id – Unique identifier for each incident report.

timestamp – Simulated time of report submission.

incident_type – Category of incident (e.g., phishing, malware, insider threat).

severity – Assigned severity level (e.g., Low, Medium, High, Critical).

affected_system – System or service impacted by the incident.

description – Synthetic narrative describing the incident.

reporter_role – Role of the reporting entity (e.g., SOC analyst, IT admin).

organization_id – Synthetic identifier for the reporting organization.

Generation Method:The dataset was synthetically generated using randomized incident attributes and structured fields to reflect realistic reporting patterns while avoiding exposure of sensitive operational data. It was designed to be representative of enterprise- and national-level reporting volumes.

Use in Experiments:

Served as input for ZKP proof generation and verification simulations.

Enabled measurement of average latency, throughput, and verification complexity under varying burst sizes (500–2,000 reports).

Supported scalability tests by scaling from 1,000 to 10,000 reports/day.

Relevance:This dataset provides a reproducible, non-sensitive benchmark for evaluating privacy-preserving, verifiable, and scalable incident reporting frameworks. It allows other researchers to replicate experiments without access to confidential incident data.