chairulridjal/arcspan-cyber-ner

Arcspan Cybersecurity NER Dataset

A multi-source cybersecurity named entity recognition dataset in OPF (OpenAI Privacy Filter) JSONL format, covering 5 entity classes across threat intelligence reports, CVE descriptions, MITRE ATT&CK entries, APT reports, and more.

Built as the training and evaluation corpus for the Arcspan project — fine-tuning OpenAI's sparse MoE Privacy Filter for cybersecurity IOC extraction.

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Dataset Summary

Split	File	Records	Spans	Purpose
R9 Train	r9_5class_train.jsonl	24,518	63,457	Main training set (latest, leakage-clean)
R9 Valid	r9_5class_valid.jsonl	2,821	5,681	Validation set
R8 Train	r8_5class_train.jsonl	26,079	76,824	Previous training set
R8 Valid	r8_5class_valid.jsonl	2,821	5,681	R8 validation set
APTNER Test	aptner_5class_test_clean.jsonl	172	340	Independent benchmark (APT reports)
CyNER Test	cyner_test.jsonl	748	892	CyNER benchmark test set
SecureBERT2 Test	securebert2_5class_test.jsonl	200	283	SecureBERT2 benchmark test set
Enriched Test	enriched_5class_test.jsonl	3,853	5,512	Held-out enriched evaluation set

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Label Space

5-class cybersecurity NER schema:

Label	Description	R9 Train Count
Indicator	IOCs — IPs, domains, URLs, file hashes, file paths, registry keys, email addresses	16,265
Malware	Malware families, ransomware, trojans, backdoors, botnets, campaigns	15,585
Organization	Threat actors, APT groups, vendors, affected organizations	13,546
System	Operating systems, software, platforms, infrastructure components	11,947
Vulnerability	CVEs, exploit names, vulnerability descriptions	6,114

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Data Format

All files are JSONL in OPF (OpenAI Privacy Filter) format. Each line is a JSON object:

{
  "text": "APT29 deployed Cobalt Strike via CVE-2021-44228 against Exchange servers.",
  "spans": {
    "Organization: APT29": [[0, 5]],
    "Malware: Cobalt Strike": [[16, 28]],
    "Vulnerability: CVE-2021-44228": [[33, 47]],
    "System: Exchange": [[56, 64]]
  },
  "info": {
    "id": "apt_reports_00042",
    "source": "apt_reports"
  }
}

Span key format: "Label: surface_text" → [[start_char, end_char], ...]

Offsets are character-level, zero-indexed, half-open [start, end).

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Training Data Sources (R9)

The R9 training set aggregates 22 sources, deduplicated and leakage-cleaned:

Source	Records	Description
cyner2_train	4,563	CyNER v2 training split
cyberner_stix_train	3,723	CyberNER harmonized (STIX-mapped)
dnrti_train	2,834	DNRTI dataset training split
aptner_train	2,584	APTNER training split
apt_reports	2,263	APT reports (LLM-annotated)
nvd_v2	1,995	NVD CVE descriptions v2 (LLM-annotated)
mitre_attack_v2	1,485	MITRE ATT&CK v2 (LLM-annotated)
synthetic_v2	1,292	Synthetically generated IOC examples v2
cyberner	1,204	CyberNER base
cyner_train	717	Original CyNER training split
defanged_augment	652	Defanged IOC augmentation (e.g. 192[.]168[.]1[.]1)
exploitdb	500	ExploitDB entries (LLM-annotated)
nvd_cve	338	NVD CVE descriptions (original)
synthetic_ioc	92	Synthetically generated IOC examples v1
vendor_blogs	61	Security vendor blog posts (LLM-annotated)
security_news	45	Security news articles (LLM-annotated)
cisa_advisories	39	CISA advisories (LLM-annotated)
mitre_attack	39	MITRE ATT&CK (original)
alienvault_otx	37	AlienVault OTX pulses (LLM-annotated)
securebert2_train	22	SecureBERT2 training split
malware_reports	21	Malware analysis reports (LLM-annotated)
dnrti_valid	12	DNRTI validation (included in train)

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Leakage Audit (R9)

Zero overlap between training data and all held-out evaluation sets:

Held-out Set	Records	Exact Overlap	Prefix-80 Overlap
R9 Validation	2,821	0	0
Enriched Test	3,853	0	0
CyNER Test	748	0	0
SecureBERT2 Test	200	0	0
APTNER Test	172	0	0

Internal duplicates: 0 exact, 0 prefix-80.

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Benchmark Evaluation Results

Evaluated using the Arcspan R8 checkpoint with strict exact-match scoring (seqeval-style):

APTNER (Independent benchmark — APT report style)

Class	F1	Precision	Recall	Support
Malware	0.707	0.793	0.637	102
Indicator	0.667	0.661	0.673	55
Vulnerability	0.500	0.429	0.600	5
Organization	0.326	0.500	0.242	91
System	0.160	0.615	0.092	87
Micro avg	0.498	0.668	0.397	340

CyNER Test

Class	F1	Precision	Recall	Support
Malware	0.577	0.585	0.570	242
System	0.399	0.412	0.387	248
Vulnerability	0.375	0.500	0.300	10
Organization	0.316	0.288	0.351	131
Indicator	0.250	0.518	0.165	261
Micro avg	0.405	0.454	0.365	892

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Usage

Loading with Hugging Face datasets

from datasets import load_dataset

ds = load_dataset("chairulridjal/arcspan-cyber-ner")
# Splits: r9_train, r9_valid, aptner_test, cyner_test, securebert2_test, enriched_test

Loading manually

import json

with open("r9_5class_train.jsonl") as f:
    examples = [json.loads(line) for line in f]

# Access spans
for ex in examples[:3]:
    print(ex["text"][:80])
    for key, offsets in ex["spans"].items():
        label, surface = key.split(": ", 1)
        for start, end in offsets:
            print(f"  [{label}] {ex['text'][start:end]!r} @ {start}:{end}")

Using with OpenAI Privacy Filter / Arcspan

# Evaluate directly with opf
opf eval r9_5class_train.jsonl \
  --checkpoint chairulridjal/arcspan \
  --device cpu

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Related Resources

• Model: chairulridjal/arcspan — Fine-tuned cybersecurity NER model
• Base model: openai/privacy-filter — OpenAI's sparse MoE Privacy Filter
• Source datasets: CyNER, APTNER, DNRTI, CyberNER, MITRE ATT&CK, NVD, ExploitDB

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License

Apache 2.0. Note that individual source datasets may carry their own licenses — see the original dataset repositories for details. LLM-annotated portions were generated from publicly available text.