Inline IPS Action Decision Validation Dataset

#code verification #control-flow analysis #policy action classification #condition coverage evaluation #IPS policy validation #security rule auditing #IPS control-flow analysis #security model evaluation

8520
0.5GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

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.

Dataset Insights

Sample Examples

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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.

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

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},
  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}
}

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