Inline IPS Action Decision Validation Dataset
- Records
- 8520
- Size
- 0.5GB
- Format
- JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
This dataset focuses on inline IPS policy source code and control-flow decisions, covering defensive actions such as allow, block, drop, quarantine, and log. Its annotations include policy priority, condition counts and coverage, control-flow paths, and expected execution results. The structured examples help assess how policy conditions and execution order determine the final action, as well as whether a model understands traffic handling, session-state changes, and subsequent processing. It supports IPS code verification, security policy auditing, and security model training and evaluation.
Overview
Dataset Insights
Preview
Sample Examples
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| matched_action | string | The final inline defense action determined from policy conditions and priority. |
| condition_total | integer | Total number of condition expressions in the source code that participate in the current decision. |
| policy_priority | integer | Policy priority parsed from source-code control flow; a smaller value indicates a higher priority. |
| condition_covered | integer | Number of condition expressions actually evaluated and covered in the given validation scenario. |
| control_flow_path | string | Key execution path from condition checks through policy matching to defense action execution, following source-code order. |
| policy_source_code | string | Original policy or control-flow source code used to execute inline IPS decisions. |
| decision_consistency | boolean | Whether the defense action parsed by the model matches the expected action inferred from source-code control flow. |
| validation_explanation | string | Explains how policy priority, condition coverage, and control flow determine the final defense action and support the validation conclusion. |
| condition_coverage_ratio | number | The proportion of covered conditions to total conditions, ranging from 0 to 1. |
| expected_execution_result | string | Expected result inferred from the source code and policy semantics, including traffic handling, session-state changes, and whether subsequent processing continues. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What does the dataset contain?
- Examples center on inline IPS decision source code and include policy priority, the matched defense action, total and covered condition counts, condition coverage ratio, control-flow path, expected execution result, and decision consistency. Actions include allow, block, drop, quarantine, and log.
- How should policy priority and condition coverage be interpreted?
- Policy priority is parsed from source-code control flow; a smaller value indicates a higher priority. The condition coverage ratio is the number of covered conditions divided by the total number of conditions, with values from 0 to 1, and indicates how thoroughly a validation scenario exercises policy conditions.
- Which training and evaluation tasks does it support?
- It supports code verification, control-flow understanding, IPS policy action classification, and condition coverage analysis. It can also evaluate whether a model can infer traffic handling, session-state changes, and whether processing continues from source code.
- How can I obtain the dataset, and what are the licensing terms?
- Check the dataset product page or the platform's delivery options for availability, formats, and licensing scope. Review the applicable license before use and confirm that it meets the requirements of your research, project, or commercial deployment.
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Cite this Work
@dataset{Mobiusi2026,
title={Inline IPS Action Decision Validation Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/1807bf13ded8d5372cd171d736575eb5?dataset_task_cate_id=1},
urldate={2026-10-27},
keywords={inline IPS dataset,IPS action decisions,intrusion prevention code verification,security policy analysis,control-flow validation,condition coverage evaluation,defense action classification,IPS model training},
version={1.0}
}Using this in research? Please cite us.