netgoat-ai/Koda-IDS-CyberReasoning

Koda IDS CyberReasoning

Dataset Details

Dataset Description

Koda IDS CyberReasoning is a multi-domain reasoning dataset designed to support the development and evaluation of AI systems for cybersecurity incident detection, investigation, analysis, and response.

The dataset contains conversational reasoning examples covering cybersecurity triage, attack-pattern analysis, incident recovery, false-positive suppression, security-related coding, tool selection, mathematics, logic, and general technical reasoning.

The dataset is intended to support the development of Koda IDS, an AI-assisted intrusion detection and cyberreasoning system focused on helping security teams understand alerts, identify potential threats, investigate suspicious activity, and recommend appropriate defensive actions.

The dataset combines examples from multiple sources and teacher-model generation pipelines. Because it contains both cybersecurity-focused and general reasoning examples, users should assess the suitability of individual subsets before using the dataset for specialized cybersecurity fine-tuning.

• Curated by: NetGoat OSS / Koda IDS project contributors
• Funded by: Not specified
• Shared by: NetGoat OSS
• Language(s) (NLP): English
• License: Apache License 2.0

Dataset Sources

• Repository: To be added
• Paper: Not available
• Demo: To be added

Uses

Direct Use

This dataset may be used for:

• Fine-tuning and instruction-tuning language models for cybersecurity reasoning.
• Developing AI-assisted intrusion detection and incident-response systems.
• Training models to analyze security alerts and suspicious events.
• Generating structured incident assessments and investigation hypotheses.
• Studying security-focused reasoning and technical problem-solving.
• Evaluating model behavior on cybersecurity triage and response tasks.
• Developing research prototypes for defensive security automation.
• Training general-purpose reasoning capabilities on technical tasks.

Cybersecurity-focused subsets should be preferred when developing specialized intrusion-detection or security-operations functionality.

Out-of-Scope Use

This dataset should not be used as the sole source of truth for:

• Fully autonomous high-impact security decisions.
• Automatic blocking, isolation, deletion, or remediation without human oversight.
• Production security operations without independent validation.
• Determining whether a person is malicious based solely on model output.
• Generating or executing unauthorized attacks.
• Developing malware, credential theft systems, or other offensive capabilities.
• Making claims about real-world incidents without verifying the underlying evidence.

The dataset is not a replacement for security expertise, live telemetry, threat-intelligence sources, or established incident-response procedures.

Dataset Structure

The dataset is distributed in JSONL format. Each line represents one example containing conversational messages and associated metadata.

The dataset may contain fields such as:

• id: Identifier associated with the example.
• messages: Conversational messages used for instruction tuning or supervised fine-tuning.
• category: Task or reasoning category.
• difficulty: Estimated task difficulty.
• source: Dataset or generation pipeline source.
• teacher_model: Model or system that generated the example.
• trace_type: Type of reasoning or response trace.
• turns: Number of conversational turns.

The messages field generally contains role-based conversational content, such as:

• system
• user
• assistant

Depending on the preprocessing stage, examples may include reasoning traces, technical explanations, code, structured incident analyses, or response recommendations.

The dataset includes multiple task categories, including:

• triage_reasoning
• attack_dna_gen
• multi_step_recovery
• false_positive_suppression
• coding_reasoning
• tool_selection
• math
• simple_logic_and_math
• Other technical or unclassified categories

The dataset may contain examples with overlapping identifiers or duplicate content. Users should run the accompanying cleaning and validation pipeline before using it for model training.

No official train, validation, or test split is currently specified. Users creating evaluation sets should use deduplication, source separation, and contamination controls to reduce data leakage.

Dataset Creation

Curation Rationale

Koda IDS CyberReasoning was created to support research and development into AI-assisted cybersecurity reasoning.

The primary motivation is to develop models that can assist with:

• Interpreting security alerts.
• Investigating suspicious behavior.
• Connecting related security events.
• Identifying possible attack patterns.
• Distinguishing potential threats from benign activity.
• Recommending defensive investigation steps.
• Explaining technical findings in a useful and structured manner.

The dataset also includes general reasoning and technical examples to support broader instruction-following and problem-solving capabilities.

Source Data

The dataset was assembled from multiple sources and teacher-model generation pipelines. The available metadata identifies sources associated with NetGoat VSA, Claude-based reasoning generation, Fable-based reasoning generation, and Kimi cyberreasoning generation.

The dataset should be considered a mixed-source synthetic and model-generated reasoning collection unless individual source records provide additional provenance information.

Data Collection and Processing

The dataset was assembled and merged from multiple reasoning sources.

The processing pipeline may include:

• JSONL formatting.
• Conversational message normalization.
• Metadata preservation.
• Category and source labeling.
• Reasoning-trace collection.
• Deduplication and quality-control processing.
• Structural validation.
• Length and consistency analysis.

Users should verify the exact preprocessing configuration used for each release.

The dataset may contain long reasoning responses, repeated content, formatting artifacts, and examples from domains outside cybersecurity. A separate cleaning and validation process is recommended before fine-tuning.

Who are the source data producers?

The source examples were produced by a combination of:

• NetGoat-related dataset-generation systems.
• Large language models used as teacher or reasoning models.
• Automated data-generation pipelines.
• Other source-specific generation processes.

The available metadata references systems associated with:

• NetGoat VSA.
• Claude-based reasoning generation.
• Fable-based reasoning generation.
• Kimi cyberreasoning generation.

Detailed information about the human authors, demographic characteristics, or individual contributors to the original source data is not available.

Annotations

The dataset contains metadata and task labels associated with individual examples. These labels may describe the task category, difficulty, source, teacher model, and reasoning-trace type.

The dataset is not known to contain comprehensive human-verified security annotations for every example.

Annotation process

Annotations and metadata were produced through a combination of dataset-generation and preprocessing pipelines.

Possible annotations include:

• Task categories.
• Difficulty labels.
• Source identifiers.
• Teacher-model identifiers.
• Reasoning-trace classifications.
• Conversation and turn metadata.

Human annotation coverage, inter-annotator agreement, and comprehensive expert validation statistics are not currently available.

Security-related outputs should therefore be treated as model-generated suggestions rather than guaranteed ground truth.

Who are the annotators?

The available information indicates that many examples and associated labels were generated or assigned by automated systems and language-model-based pipelines.

The identities of individual human annotators, if any, are not documented.

Personal and Sensitive Information

The dataset is intended for technical and cybersecurity reasoning tasks. However, examples may contain simulated or synthetic security events, identifiers, hostnames, IP addresses, domains, log fragments, code, or other technical artifacts.

Some examples may resemble real-world security data. Users should inspect and scan each release for:

• Personal information.
• Credentials or authentication tokens.
• API keys.
• Private infrastructure details.
• Email addresses.
• IP addresses.
• Domain names.
• Sensitive logs.
• Proprietary code or configuration data.

No comprehensive privacy audit or formal anonymization guarantee is currently documented.

Users should perform their own privacy and secret-scanning checks before redistributing or deploying the dataset.

Bias, Risks, and Limitations

Koda IDS CyberReasoning has several important limitations:

• Synthetic-data bias: Many examples may be generated by language models rather than collected from real security operations.
• Teacher-model bias: The reasoning style, assumptions, errors, and preferences of the source models may be reflected in the data.
• Domain imbalance: The dataset contains both cybersecurity and non-cybersecurity tasks.
• Reasoning-trace artifacts: Some examples may contain verbose or inconsistent internal-style reasoning traces.
• Duplicate content: Multiple records may contain identical or highly similar content.
• Metadata inconsistencies: Identifiers, categories, or source metadata may require validation.
• Potential hallucinations: Security-related answers may include unsupported claims, invented telemetry, or inaccurate technical explanations.
• Limited real-world grounding: Synthetic examples may not reflect the complexity, ambiguity, or operational constraints of real security incidents.
• Distribution shift: Model performance may degrade when handling unfamiliar infrastructure, attack patterns, log formats, or environments.
• Evaluation contamination: Duplicate or near-duplicate examples may cause overly optimistic evaluation results.
• Uneven response lengths: Some categories may contain much longer responses than others, which can affect training efficiency and sampling balance.
• Incomplete provenance: The origin and verification status of some examples may not be fully documented.

A model fine-tuned on this dataset may produce plausible but incorrect security recommendations. Model outputs should not be treated as verified incident findings without independent evidence.

Recommendations

Users should:

• Run structural validation and deduplication before training.
• Separate cybersecurity examples from general reasoning examples.
• Create independent validation and test sets.
• Check for train–test contamination.
• Review security examples for factual correctness.
• Validate security claims against logs, telemetry, and trusted references.
• Use human approval for high-impact security actions.
• Avoid granting models unrestricted access to production infrastructure.
• Scan the dataset for secrets and personal information.
• Monitor model behavior after fine-tuning.
• Evaluate false positives, false negatives, calibration, and uncertainty.
• Preserve the original dataset and document all preprocessing operations.
• Treat model-generated incident assessments as hypotheses until verified.

Citation

No paper or formal publication currently introduces this dataset.

BibTeX:

@dataset{koda_ids_cyberreasoning,
  title        = {Koda IDS CyberReasoning},
  author       = {{NetGoat OSS}},
  year         = {2026},
  publisher    = {NetGoat OSS},
  license      = {Apache-2.0},
  note         = {Multi-domain cybersecurity and technical reasoning dataset}
}

APA:

NetGoat OSS. (2026). Koda IDS CyberReasoning (Dataset). Licensed under the Apache License 2.0.

Glossary

• Cyberreasoning: The process of analyzing cybersecurity information, identifying possible threats, and explaining potential defensive actions.

• IDS: Intrusion Detection System; a system that monitors activity to identify potentially malicious or suspicious behavior.

• Triage: The process of prioritizing and assessing security alerts or incidents.

• Attack DNA: A structured representation of characteristics, indicators, and behaviors associated with an attack or intrusion pattern.

• False-positive suppression: Identifying alerts that appear suspicious but are likely benign or incorrectly triggered.

• Multi-step recovery: Reasoning about a sequence of actions used to investigate, contain, recover from, or learn from an incident.

• Reasoning trace: A model-generated explanation or intermediate reasoning record associated with an answer.

• Teacher model: A model used to generate examples or provide supervision for another model.

• Synthetic data: Data generated by software, simulations, or models rather than directly collected from real-world events.

• Data contamination: The presence of overlapping or highly similar examples between training and evaluation datasets.

• Distribution shift: A difference between the data used during training and the data encountered after deployment.

More Information

Koda IDS CyberReasoning is part of the broader Koda IDS and NetGoat OSS ecosystem.

The project focuses on developing open-source tools and models for cybersecurity analysis, intrusion detection, and defensive automation.

Dataset Card Authors

NetGoat OSS / Koda IDS project contributors