Scikit-learn
Joblib
English
tfidf_logistic_logsource_classifier
cybersecurity
sigma
detection-engineering
mitre-attack
gradio
enterprise
Instructions to use alirezaaminzadeh/sigmaforge-logsource-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use alirezaaminzadeh/sigmaforge-logsource-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("alirezaaminzadeh/sigmaforge-logsource-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Upload logsource classifier
Browse files- .gitattributes +1 -0
- README.md +32 -0
- classifier_config.json +80 -0
- config.json +8 -0
- embedding_model.txt +1 -0
- faiss.index +3 -0
- logsource_classifier.joblib +3 -0
- retriever_vectorizer.joblib +3 -0
- rule_embeddings.npy +3 -0
- tfidf_vectorizer.joblib +3 -0
- train_metrics.json +5 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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faiss.index filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: mit
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language:
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- en
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tags:
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- cybersecurity
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- sigma
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- detection-engineering
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- mitre-attack
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- gradio
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- enterprise
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library_name: sklearn
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---
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# SigmaForge Logsource Classifier
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TF-IDF + Logistic Regression classifier that maps detection hypotheses to Sigma `logsource` fields (product, service, category).
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## Intended Use
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- Classify log source for Sigma rule generation pipeline
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- Not for production SIEM routing without retraining on your environment
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## Training
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Trained on synthetic hypothesis ↔ logsource pairs derived from SigmaHQ rule corpus.
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## Limitations
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- CPU-only inference
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- Best performance on Windows/Sysmon/DNS hypotheses
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- Heuristic fallback when model artifacts unavailable
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classifier_config.json
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{
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"idx_to_label": {
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"0": "aws|cloudtrail",
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"1": "azure|activitylogs",
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"2": "azure|auditlogs",
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"3": "azure|pim",
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"4": "azure|riskdetection",
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"5": "azure|signinlogs",
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"6": "bitbucket|audit",
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"7": "cisco|duo",
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"8": "gcp|gcp.audit",
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"9": "gcp|google_workspace.admin",
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"10": "gcp|google_workspace.login",
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"11": "github|audit",
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"12": "jvm",
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"13": "kubernetes|audit",
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"14": "linux",
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"15": "linux|auditd",
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"16": "linux|clamav",
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"17": "linux|guacamole",
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"18": "linux|sshd",
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"19": "linux|syslog",
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"20": "linux|vsftpd",
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"21": "m365|audit",
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"22": "m365|exchange",
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"23": "m365|threat_management",
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"24": "nodejs",
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"25": "okta|okta",
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"26": "onelogin|onelogin.events",
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"27": "opencanary",
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"28": "python",
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"29": "rpc_firewall",
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"30": "ruby_on_rails",
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"31": "spring",
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"32": "sql",
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"33": "velocity",
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"34": "windows",
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"35": "windows|security",
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"36": "windows|sysmon"
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},
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"labels": [
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"aws|cloudtrail",
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"azure|activitylogs",
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"azure|auditlogs",
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"azure|pim",
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"azure|riskdetection",
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"azure|signinlogs",
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"bitbucket|audit",
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"cisco|duo",
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"gcp|gcp.audit",
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"gcp|google_workspace.admin",
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"gcp|google_workspace.login",
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"github|audit",
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"jvm",
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"kubernetes|audit",
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"linux",
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"linux|auditd",
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"linux|clamav",
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"linux|guacamole",
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"linux|sshd",
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"linux|syslog",
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"linux|vsftpd",
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"m365|audit",
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"m365|exchange",
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"m365|threat_management",
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"nodejs",
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"okta|okta",
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"onelogin|onelogin.events",
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"opencanary",
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"python",
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"rpc_firewall",
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"ruby_on_rails",
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"spring",
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"sql",
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"velocity",
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"windows",
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"windows|security",
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"windows|sysmon"
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]
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}
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config.json
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{
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"engine_version": "1.0.0",
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"model_type": "tfidf_logistic_logsource_classifier",
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"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
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"train_samples": 406,
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"labels": 37,
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"accuracy": 0.9631
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}
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embedding_model.txt
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sentence-transformers/all-MiniLM-L6-v2
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faiss.index
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version https://git-lfs.github.com/spec/v1
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oid sha256:cee2c87d9a03103a5b2ba53f8b399c11727f218798c9583c239b496968d0be46
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size 780333
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logsource_classifier.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:4f620eb1c1c5fb5c6dcf39bc7321aa3adeea87d6d27737f36cabb6e182ca8b2e
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size 607560
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retriever_vectorizer.joblib
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:c47cebcd7b4377ed24dd0f8d6c004a28d1cac0086ed51bc5f619e03e88045e1f
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size 14113
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rule_embeddings.npy
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:38cb32f65a5bb0fc0ca8349566c57c8da50e0203bfc4ea91ddaa32375b22bafd
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size 780416
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tfidf_vectorizer.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:65714e0bbefe137e995363ef9f7b150592bf43cfe02ec14f6dc4e43b88dc29d9
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size 77072
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train_metrics.json
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{
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"train_samples": 406,
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"labels": 37,
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"accuracy": 0.9631
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}
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