Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
title: string
id: string
licenses: list<item: struct<name: string>>
child 0, item: struct<name: string>
child 0, name: string
isPrivate: bool
description: string
files: list<item: struct<name: string, description: string>>
child 0, item: struct<name: string, description: string>
child 0, name: string
child 1, description: string
objects: list<item: struct<type: string, spec_version: string, id: string, created: timestamp[s], modified: t (... 66 chars omitted)
child 0, item: struct<type: string, spec_version: string, id: string, created: timestamp[s], modified: timestamp[s] (... 54 chars omitted)
child 0, type: string
child 1, spec_version: string
child 2, id: string
child 3, created: timestamp[s]
child 4, modified: timestamp[s]
child 5, name: string
child 6, pattern: string
child 7, pattern_type: string
type: string
spec_version: string
to
{'type': Value('string'), 'id': Value('string'), 'spec_version': Value('string'), 'objects': List({'type': Value('string'), 'spec_version': Value('string'), 'id': Value('string'), 'created': Value('timestamp[s]'), 'modified': Value('timestamp[s]'), 'name': Value('string'), 'pattern': Value('string'), 'pattern_type': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
title: string
id: string
licenses: list<item: struct<name: string>>
child 0, item: struct<name: string>
child 0, name: string
isPrivate: bool
description: string
files: list<item: struct<name: string, description: string>>
child 0, item: struct<name: string, description: string>
child 0, name: string
child 1, description: string
objects: list<item: struct<type: string, spec_version: string, id: string, created: timestamp[s], modified: t (... 66 chars omitted)
child 0, item: struct<type: string, spec_version: string, id: string, created: timestamp[s], modified: timestamp[s] (... 54 chars omitted)
child 0, type: string
child 1, spec_version: string
child 2, id: string
child 3, created: timestamp[s]
child 4, modified: timestamp[s]
child 5, name: string
child 6, pattern: string
child 7, pattern_type: string
type: string
spec_version: string
to
{'type': Value('string'), 'id': Value('string'), 'spec_version': Value('string'), 'objects': List({'type': Value('string'), 'spec_version': Value('string'), 'id': Value('string'), 'created': Value('timestamp[s]'), 'modified': Value('timestamp[s]'), 'name': Value('string'), 'pattern': Value('string'), 'pattern_type': Value('string')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Daily Threat Intelligence STIX 2.1 Dataset
A serverless, daily-updating Threat Intelligence Harvester and Normalization Pipeline powered by Autonomous AI Security Analyst Agents, PyTorch Graph Neural Networks (GNN), Security LLMs, GitHub Zero-Day PoC Hunter, and 200,000+ historical cybersecurity records. Ingests real-time indicators across 5 major security feeds, enriches CVEs with FIRST.org EPSS exploit prediction scores, classifies unstructured text into MITRE ATT&CK tactics via NLP, detects ransomware group surge anomalies using IsolationForest models, exports multi-format artifacts (STIX 2.1 JSON, CSV, Parquet, Suricata Rules, 3D WebGL Graph HTML), and publishes public datasets on Kaggle and Hugging Face.
Load Dataset via Python (1 Line)
from datasets import load_dataset
dataset = load_dataset("elsiddik/daily-threat-intel-stix21")
Autonomous AI Security Analyst Agent (src/threat_agent.py)
The project features a natural language AI Security Analyst Agent that allows SOC analysts and security engineers to query live threat datasets, PyTorch Knowledge Graphs, and EPSS exploit scores in plain English directly on Kaggle GPUs:
from src.threat_agent import ThreatIntelAgent
agent = ThreatIntelAgent(records)
response = agent.query("What are the top critical CVEs actively exploited today?")
print(response["answer"])
Features
- Autonomous AI Security Analyst Agent (
src/threat_agent.py): Interactive natural language RAG threat query engine. - GitHub Zero-Day PoC Exploit Hunter (
src/poc_hunter.py): Automatically discovers live exploit PoC scripts targeting CISA KEV vulnerabilities. - Interactive 3D WebGL Threat Knowledge Graph (
src/graph_visualizer.py): Renders an interactive 3D WebGL network graph (threat_graph_3d.html). - Multi-Feed Async Ingestion (
src/fetchers.py): CISA KEV, URLhaus, ThreatFox, Ransomwatch, and GHSA. - EPSS Exploitation Scoring (
src/enricher.py): FIRST.org EPSS exploit probability scores. - PyTorch GNN Knowledge Graph (
src/gnn_threat_graph.py): GCN link predictor for APT infrastructure attribution. - STIX 2.1 Compliance (
src/stix_builder.py): OASIS STIX 2.1 compliant JSON bundle builder. - Multi-Format Artifact Export (
src/exporter.py): CSV, Parquet, STIX 2.1 JSON, Suricata Rules, YARA Rules, Sigma Rules, 3D WebGL HTML Graph.
License
Licensed under Apache 2.0. Built for the cybersecurity community.
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