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CyberSecurity-100B

A large-scale, quality-filtered cybersecurity corpus extracted from Nemotron-CC-v2, Ultra-FineWeb, and Fineweb-Edu-Chinese-V2.1, designed for continual pre-training (CPT) of large language models on cybersecurity domain knowledge. Contains ~4.6B English tokens and ~5.6B Chinese tokens across quality-tiered and code-aware splits, with multi-signal scoring and structured metadata.

Disclaimer: This dataset is provided for academic research only. All content is extracted from publicly available web data. The views, opinions, and information expressed in the dataset content do not represent the views or positions of the research team. The research team does not endorse, support, or take responsibility for any of the content. Users access and use this dataset at their own risk.

Security Notice: This dataset contains information about cybersecurity vulnerabilities, exploitation techniques, and offensive security methods. This information is already publicly available and is collected here solely for defensive security research and education. Misuse of this information to attack systems without authorization is illegal. Users must comply with all applicable laws and regulations.

Dataset Summary

CyberSecurity-100B is produced by a multi-stage pipeline (cyberhunter) that extracts, scores, and categorizes cybersecurity-relevant content from large-scale web crawls. The pipeline applies:

Lexical recall filtering — keyword and high-value-phrase matching with hard-negative exclusion
ML-based cyber relevance scoring — sklearn logistic regression classifier (v2)
CPT-worthiness scoring — rule-based quality assessment with grouped regex patterns
Code-aware detection — identifies 7 code/technical content types with false-positive control
Topic classification — 10–18 cybersecurity topic categories
MinHash near-dedup — Jaccard similarity deduplication at 0.85 threshold

The result is a quality-tiered, code-aware corpus optimized for continual pre-training of cybersecurity-capable LLMs.

Supported Tasks

Continual pre-training (CPT): Domain-adapt LLMs to cybersecurity
Language modeling: Pre-train or fine-tune on cybersecurity domain text
Security code generation: Train on code-aware splits (security rules, exploits, configurations)
Text classification: Leverage cyber_score and topic labels for downstream tasks
Information extraction: Extract IOCs, CVEs, TTPs using code detection metadata

Dataset Structure

CyberSecurity-100B/
├── Nemotron-CC-v2 (EN) ─────────────────────────────────────
│   ├── high_precision_code/    # cyber>=0.93, cpt>=0.75, has_code=True
│   ├── high_precision_text/    # cyber>=0.93, cpt>=0.75, has_code=False
│   ├── balanced_code/          # cyber 0.85-0.93, cpt>=0.60, has_code=True
│   ├── balanced_text/          # cyber 0.85-0.93, cpt>=0.60, has_code=False
│   └── general_cyber/          # cyber>=0.85, cpt<0.60
├── Ultra-FineWeb-L3 (ZH) ──────────────────────────────────
│   ├── zh_high_precision/      # cyber>=0.93
│   ├── zh_balanced/            # cyber>=0.78
│   └── zh_full_coverage/       # cyber>=0.50
├── Ultra-FineWeb (EN + ZH, MinHash deduped) ──────────────
│   ├── ufw_en_high_precision/  # EN cyber>=0.93
│   ├── ufw_en_balanced/        # EN cyber>=0.85
│   ├── ufw_en_general/         # EN cyber>=0.78
│   ├── ufw_zh_high_precision/  # ZH cyber>=0.93
│   ├── ufw_zh_balanced/        # ZH cyber>=0.85
│   └── ufw_zh_general/         # ZH cyber>=0.78
└── Fineweb-Edu-Chinese-V2.1 (ZH, MinHash deduped) ───────
    ├── fwedu_4_5_high_precision/  # edu 4-5, cyber>=0.93
    ├── fwedu_4_5_balanced/        # edu 4-5, cyber>=0.85
    ├── fwedu_4_5_general/         # edu 4-5, cyber>=0.78
    ├── fwedu_3_4_high_precision/  # edu 3-4, cyber>=0.93
    ├── fwedu_3_4_balanced/        # edu 3-4, cyber>=0.85
    ├── fwedu_3_4_general/         # edu 3-4, cyber>=0.78
    ├── fwedu_2_3_high_precision/  # edu 2-3, cyber>=0.93
    ├── fwedu_2_3_balanced/        # edu 2-3, cyber>=0.85
    └── fwedu_2_3_general/         # edu 2-3, cyber>=0.78

Split Criteria

Nemotron-CC-v2 (EN)

Split	Cyber Score	CPT Score	Has Code	Recommended Use
high_precision_code	>= 0.93	>= 0.75	Yes	Core CPT — highest quality with code
high_precision_text	>= 0.93	>= 0.75	No	Core CPT — highest quality prose
balanced_code	0.85–0.93	>= 0.60	Yes	Extended CPT — code content, moderate confidence
balanced_text	0.85–0.93	>= 0.60	No	Extended CPT — prose, moderate confidence
general_cyber	>= 0.85	< 0.60	Either	Supplementary — cyber-relevant but lower quality

Ultra-FineWeb-L3 (ZH)

Split	Cyber Score	Recommended Use
zh_high_precision	>= 0.93	Highest quality ZH cyber docs
zh_balanced	>= 0.78	Quality-quantity balance
zh_full_coverage	>= 0.50	Maximum recall

Ultra-FineWeb (EN + ZH, MinHash deduped)

Split	Language	Cyber Score	Recommended Use
ufw_en_high_precision	EN	>= 0.93	Highest quality EN web cyber
ufw_en_balanced	EN	>= 0.85	Quality-quantity balance
ufw_en_general	EN	>= 0.78	General coverage
ufw_zh_high_precision	ZH	>= 0.93	Highest quality ZH web cyber
ufw_zh_balanced	ZH	>= 0.85	Quality-quantity balance
ufw_zh_general	ZH	>= 0.78	General coverage

Fineweb-Edu-Chinese-V2.1 (ZH, MinHash deduped)

Split	Edu Band	Cyber Score	Recommended Use
fwedu_4_5_high_precision	4-5	>= 0.93	Highest edu quality, highest cyber confidence
fwedu_4_5_balanced	4-5	>= 0.85	Highest edu quality, balanced
fwedu_4_5_general	4-5	>= 0.78	Highest edu quality, general coverage
fwedu_3_4_high_precision	3-4	>= 0.93	Medium edu quality, highest cyber confidence
fwedu_3_4_balanced	3-4	>= 0.85	Medium edu quality, balanced
fwedu_3_4_general	3-4	>= 0.78	Medium edu quality, general coverage
fwedu_2_3_high_precision	2-3	>= 0.93	Broader edu quality, highest cyber confidence
fwedu_2_3_balanced	2-3	>= 0.85	Broader edu quality, balanced
fwedu_2_3_general	2-3	>= 0.78	Broader edu quality, general coverage

Data Instances

Each JSONL record contains full text with rich metadata:

{
  "id": "b38f5ed2bc1c8b807fa7830f15ad1f9f...",
  "source": "nvidia/Nemotron-CC-v2",
  "source_path": "v1/High-Quality/part_000000.parquet",
  "source_subset": "organic_cc",
  "url": "https://example.com/blog/cve-analysis",
  "domain": "example.com",
  "text": "A remote code execution vulnerability exists in...",
  "language": "en",
  "char_count": 7496,
  "token_count_est": 1874,
  "cyber_score": 0.9756,
  "topic": "vulnerability",
  "topic_confidence": 0.8,
  "cpt_worthy_score": 0.8420,
  "cpt_worthy_label": "high",
  "has_code": true,
  "code_score": 0.65,
  "code_types": ["security_rules", "programming_code"],
  "code_line_count_est": 42,
  "code_block_count_est": 3,
  "command_count_est": 5,
  "ioc_count_est": 8,
  "code_detection_reasons": ["matched_security_rules", "matched_programming_code_strong"],
  "pipeline_version": "cyberhunter_v2"
}

Data Fields

Field	Type	Description
`id`	string	SHA-256 based document identifier
`source`	string	Source dataset (nvidia/Nemotron-CC-v2, openbmb/Ultra-FineWeb, etc.)
`source_path`	string	Original parquet file path
`source_subset`	string	Source subset identifier
`url`	string	Original URL (may be empty)
`domain`	string	URL domain (may be empty)
`text`	string	Full document text
`language`	string	Language code (en, zh)
`char_count`	int	Character count
`token_count_est`	int	Estimated token count (chars / 4)
`cyber_score`	float	Cyber relevance score from ML classifier [0, 1]
`topic`	string	Cybersecurity topic category
`topic_confidence`	float	Topic classification confidence [0, 1]
`cpt_worthy_score`	float	CPT-worthiness quality score [0, 1]
`cpt_worthy_label`	string	Quality label (high, medium, low, reject)
`has_code`	bool	Whether code/technical content detected
`code_score`	float	Code content detection score [0, 1]
`code_types`	list[string]	Detected code types (see below)
`code_line_count_est`	int	Estimated lines of code
`code_block_count_est`	int	Estimated code blocks (fenced/indented)
`command_count_est`	int	Estimated CLI commands
`ioc_count_est`	int	Estimated indicators of compromise
`code_detection_reasons`	list[string]	Reasons for code detection
`pipeline_version`	string	Pipeline version identifier

Topic Categories

English (10 categories)

Topic	Description
malware_analysis	Malware analysis and reverse engineering
vulnerability	Vulnerability disclosure and analysis
threat_intelligence	Threat landscape and APT analysis
exploit_writeup	Exploit development and PoC code
incident_response	Incident handling and DFIR
digital_forensics	Forensic analysis and evidence
detection_engineering	Detection rules and hunting
network_security	Network security analysis
identity_access	Identity and access management
application_security	Application security testing

Chinese (18 categories)

All English categories plus: cryptography_security, ctf_training, security_tools, offensive_security, secure_coding, compliance_policy, cloud_security, general_cybersecurity

Code Types

Detected by the code-aware module with false-positive control (single IP/hash/command does NOT trigger has_code):

Code Type	Description	Examples
security_rules	Security detection rules	YARA, Sigma, Snort/Suricata, Splunk SPL
configuration	Infrastructure configuration	YAML, nginx/apache, K8s, Dockerfile, iptables
programming_code	Source code snippets	Python, C/C++, JS, Java, Go, Rust, PHP, Ruby, Shell
exploit_or_poc_code	Exploit scripts and PoC	Payload generation, shellcode, exploit frameworks
logs_and_iocs	Log data and IOCs	Log lines, stack traces, IP/hash indicators, Sysmon
command_line	CLI commands and tool output	nmap, sqlmap, yara, security tool invocations
patch_or_diff	Patches and diffs	git diff, unified diff, CVE patch snippets

Data Sources & Statistics

Source 1: Nemotron-CC-v2 (English)

Extracted from 88.2B English documents in Nemotron-CC-v2.

Split	Documents	Est. Tokens
high_precision_code	—	—
high_precision_text	—	—
balanced_code	—	—
balanced_text	—	—
general_cyber	—	—
Total	~4.2M	~4.4B

Source 2: Ultra-FineWeb-L3 (Chinese)

Extracted from 359M Chinese documents in Ultra-FineWeb-L3.

Split	Cyber Score	Documents	Est. Tokens
zh_high_precision	>= 0.93	1,668	774K
zh_balanced	>= 0.78	21,202	7.4M
zh_full_coverage	>= 0.50	123,487	38.2M
Total		146,357	46.4M

Source 3: Ultra-FineWeb (English + Chinese, MinHash deduped)

Extracted from Ultra-FineWeb. All splits are MinHash deduped (Jaccard >= 0.85) and include code detection.

Split	Language	Cyber Score	Documents	Est. Tokens	Has Code
ufw_en_high_precision	EN	>= 0.93	19,270	26.3M	5,804
ufw_en_balanced	EN	>= 0.85	42,213	56.8M	11,929
ufw_en_general	EN	>= 0.78	60,554	80.8M	16,846
ufw_zh_high_precision	ZH	>= 0.93	50,559	36.5M	7,791
ufw_zh_balanced	ZH	>= 0.85	105,182	65.4M	10,824
ufw_zh_general	ZH	>= 0.78	148,396	84.8M	13,031
EN subtotal			122,037	164.0M	34,579
ZH subtotal			304,137	186.7M	31,646

Source 4: Fineweb-Edu-Chinese-V2.1 (Chinese, MinHash deduped)

Extracted from Fineweb-Edu-Chinese-V2.1 — the largest Chinese educational web corpus. Data sources are disjoint from Ultra-FineWeb, covering CCI3, SkyPile, WuDao, Map-CC, IndustryCorpus2, OpenCSG-CC, etc. All splits are MinHash deduped (Jaccard >= 0.85) and include code detection.

Split	Edu Band	Cyber Score	Documents	Est. Tokens	Has Code
fwedu_4_5_high_precision	4-5	>= 0.93	23,320	45.2M	1,425
fwedu_4_5_balanced	4-5	>= 0.85	66,108	108.5M	2,323
fwedu_4_5_general	4-5	>= 0.78	99,716	154.7M	2,728
fwedu_3_4_high_precision	3-4	>= 0.93	317,582	495.8M	34,664
fwedu_3_4_balanced	3-4	>= 0.85	786,036	1,032.9M	47,207
fwedu_3_4_general	3-4	>= 0.78	1,154,106	1,416.6M	53,064
fwedu_2_3_high_precision	2-3	>= 0.93	201,655	250.5M	19,676
fwedu_2_3_balanced	2-3	>= 0.85	661,999	668.7M	31,741
fwedu_2_3_general	2-3	>= 0.78	1,133,271	1,052.5M	39,520
Total			4,443,793	5,225.4M	232,348

FW-Edu Aggregate by Quality Profile

Quality Profile	>= 0.93 (High Precision)	>= 0.85 (Balanced)	>= 0.78 (General)
4_5 band	23,320 docs	66,108 docs	99,716 docs
3_4 band	317,582 docs	786,036 docs	1,154,106 docs
2_3 band	201,655 docs	661,999 docs	1,133,271 docs
Total	542,557 docs	1,514,143 docs	2,387,093 docs

Grand Total

Source	Language	Documents	Est. Tokens
Nemotron-CC-v2	EN	~4.2M	~4.4B
Ultra-FineWeb	EN	122,037	164.0M
Ultra-FineWeb-L3	ZH	146,357	46.4M
Ultra-FineWeb	ZH	304,137	186.7M
Fineweb-Edu-Chinese-V2.1	ZH	4,443,793	5.23B
Total		~9.3M	~10.2B

Pipeline Details

The cyberhunter pipeline processes data in the following stages:

Recall filtering — Precompiled regex + frozenset keyword matching with hard-negative exclusion
Lexical pre-ranking — Cyber-relevant term frequency scoring with configurable threshold
ML classification — Logistic regression (sklearn) with embedding features for cyber relevance scoring
Topic classification — Rule-based topic assignment across 10–18 cybersecurity categories
CPT-worthiness scoring — Grouped regex alternation across 11 positive and 8 negative quality groups
Code-aware detection — 7 code type detectors with strong/weak indicator classification and false-positive control
MinHash near-dedup — datasketch MinHashLSH at Jaccard >= 0.85 (shingle_size=5, num_perm=128)

English Pipeline

Classifier: TF-IDF + Logistic Regression on English embeddings
Source: Nemotron-CC-v2 (88.2B docs) + Ultra-FineWeb (1.16B docs)
Recall rate: ~0.005% from Nemotron, ~0.22% from UFW

Chinese Pipeline

Classifier: jieba word segmentation + TF-IDF (1-2gram, 500k features) + Logistic Regression
Version: v2 with 5,000+ hard negatives (食品安全, 网络游戏, 生产安全, etc.)
Performance: F1=0.998, PR-AUC=0.9999, Precision=1.0 at threshold >= 0.80
Sources: Ultra-FineWeb-L3 (359M ZH docs), Ultra-FineWeb (131M ZH docs), Fineweb-Edu-Chinese-V2.1 (977M docs)

Considerations

Academic Use Only

This dataset is compiled and distributed strictly for academic, non-commercial research purposes. Any commercial use, redistribution for profit, or application in commercial products is strictly prohibited without explicit written authorization. The research team receives no financial benefit from this dataset.

Disclaimer

The content in this dataset is extracted from publicly available web data and represents the views of the original authors, not the research team. The research team:

Does not endorse, verify, or guarantee the accuracy of any content
Does not take responsibility for any claims, opinions, or information in the dataset
Does not encourage or support the use of this information for unauthorized access or illegal activities
Makes no warranties, express or implied, regarding the dataset's fitness for any particular purpose

Security Risk Notice

This dataset contains technical information about vulnerabilities, exploitation methods, and offensive security techniques. While this information is already publicly available, users should be aware that:

Unauthorized use of exploit techniques against systems you do not own or have explicit permission to test is illegal in most jurisdictions
Responsible disclosure practices should be followed when discovering new vulnerabilities
Users must comply with all applicable local, national, and international laws
The dataset should only be used to improve defensive security capabilities

Licensing

The dataset compilation is released under Apache 2.0 for academic, non-commercial use. Individual content items originate from publicly available web data and retain their original source licensing. Users must verify licensing for specific content before any redistribution. Commercial use is prohibited.

Biases

Language bias: Primarily English + Chinese; other languages not covered
Source bias: Content reflects the distribution of underlying web crawls
Topic imbalance: malware_analysis and vulnerability dominate scored docs
Score calibration: Cyber scores are ML-predicted and may have domain-specific miscalibration
Code detection: Rule-based detection may miss implicit code references or flag false positives

Citation

@dataset{cybersecurity-100b,
  title={CyberSecurity-100B: A Quality-Filtered Cybersecurity Corpus for Continual Pre-Training},
  author={WhitzardAgent Team (SIIxFudan)},
  year={2026},
  note={v4: Added Fineweb-Edu-Chinese-V2.1 cybersecurity data (~5.2B ZH tokens)},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/WhitzardAgent/CyberSecurity-100B}
}