Replace dataset with 41.6M-pipeline output (UC fixes) — phase 1

Phase 1 of UC-feedback regeneration:
- New README with full ground-truth, scoring, and MITRE documentation
- Delete stale signal shards (21 new shards uploaded in phase 2)
- Delete placeholder graph.graphml + graph.json (replaced by per-incident GraphMLs)
- New nodes.jsonl (44,717 nodes), attack_reports.jsonl (12,361 reports)

.gitattributes

@@ -62,3 +62,4 @@ graph/edges.jsonl filter=lfs diff=lfs merge=lfs -text
 graph/graph.graphml filter=lfs diff=lfs merge=lfs -text
 graph/graph.json filter=lfs diff=lfs merge=lfs -text
 graph/incidents.jsonl filter=lfs diff=lfs merge=lfs -text
+graph/attack_reports.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -18,12 +18,12 @@ tags:
   - labeled-dataset
   - lead-rules
 size_categories:
-  - 100M<n<1B
+  - 10M<n<100M
 dataset_info:
   - config_name: signals
     splits:
       - name: train
-        num_examples: 114074530
+        num_examples: 41658748
 configs:
   - config_name: signals
     data_files:
@@ -43,15 +43,15 @@ configs:
         path: graph/incidents.jsonl
 ---
 
-# WitFoo Precinct6 Cybersecurity Dataset (114M)
+# WitFoo Precinct6 Cybersecurity Dataset (large)
 
 ## Overview
 
-A large-scale, labeled cybersecurity dataset derived from production Security Operations Center (SOC) data processed by [WitFoo Precinct](https://www.witfoo.com/) version 6.x. This dataset contains **114 million sanitized security events** (signal logs) and **provenance graphs** (10,442 incident graphs with 23,362 nodes and 32,732,650 edges) from real enterprise network monitoring across 5 organizations.
+A large-scale, labeled cybersecurity dataset derived from production Security Operations Center (SOC) data processed by [WitFoo Precinct](https://www.witfoo.com/) version 6.x. This dataset contains **41,658,748 sanitized security events** (signal logs) across 5 organizations and **12,361 incident provenance graphs** (44,717 nodes, 9,494,858 edges).
 
 **Available in two sizes:**
-- [`witfoo/precinct6-cybersecurity`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity) — 2M signals (smaller, faster to load)
-- [`witfoo/precinct6-cybersecurity-100m`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m) — **114M signals (this dataset)**
+- [`witfoo/precinct6-cybersecurity`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity) — 2.1M signals (smaller, faster to load; same incidents and graph methodology)
+- [`witfoo/precinct6-cybersecurity-100m`](https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m) — **41.6M signals (this dataset)**
 
 **Generate your own:** WitFoo Precinct 6.x customers can create datasets from their own data using the open-source pipeline: [`witfoo/dataset-from-precinct6`](https://github.com/witfoo/dataset-from-precinct6)
 
@@ -67,23 +67,22 @@ This dataset is designed to support research in:
 ```python
 from datasets import load_dataset
 
-# Load flat signal logs (114M rows across 58 Parquet shards)
+# Load flat signal logs (41.6M rows across 21 Parquet shards)
 signals = load_dataset("witfoo/precinct6-cybersecurity-100m", "signals", split="train")
 
-# Find malicious events (from confirmed incidents)
-malicious = signals.filter(lambda x: x["label_binary"] == "malicious")
+# Find analyst-confirmed malicious events
+malicious_confirmed = signals.filter(
+    lambda x: x["label_binary"] == "malicious" and x["disposition"] == "Disrupted"
+)
 
-# Find suspicious events (matched detection rules)
+# Find suspicious events that matched detection rules but are not in confirmed incidents
 suspicious = signals.filter(lambda x: x["label_binary"] == "suspicious")
 
-# Query by product/vendor
-cisco_events = signals.filter(lambda x: x["vendor_name"] == "Cisco")
-
-# Load provenance graph
+# Load provenance graph (44.7k nodes, 9.5M edges)
 nodes = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_nodes", split="train")
 edges = load_dataset("witfoo/precinct6-cybersecurity-100m", "graph_edges", split="train")
 
-# Load full incident graphs
+# Load full incident graphs (12,361 incidents with embedded artifacts, leads, frameworks)
 incidents = load_dataset("witfoo/precinct6-cybersecurity-100m", "incidents", split="train")
 ```
 
@@ -91,9 +90,17 @@ incidents = load_dataset("witfoo/precinct6-cybersecurity-100m", "incidents", spl
 
 | Label | Count | Percentage |
 |-------|-------|------------|
-| `benign` | 113,326,050 | 99.34% |
-| `malicious` | 125,780 | 0.11% |
-| `suspicious` | 622,700 | 0.55% |
+| `benign` | 41,447,721 | 99.49% |
+| `malicious` | 148,128 | 0.36% |
+| `suspicious` | 62,899 | 0.15% |
+
+Disposition breakdown (the raw Precinct status, exposed for ground-truth stratification — see [Ground Truth](#ground-truth-and-disposition)):
+
+| Disposition | Count | Meaning |
+|-------------|-------|---------|
+| `Unprocessed` | 41,575,946 | No analyst review (default state for benign/suspicious + un-reviewed malicious) |
+| `Disrupted` | 82,782 | SOC analyst confirmed and intervened |
+| `Dismissed` | 20 | SOC analyst dismissed |
 
 ## Signal Columns
 
@@ -101,7 +108,7 @@ incidents = load_dataset("witfoo/precinct6-cybersecurity-100m", "incidents", spl
 |--------|------|-------------|
 | `timestamp` | float | Unix epoch timestamp |
 | `message_type` | string | Event classification (e.g., `firewall_action`, `account_logon`, `AssumeRole`) |
-| `stream_name` | string | Source product/data stream (see Source Products below) |
+| `stream_name` | string | Source product/data stream |
 | `pipeline` | string | Ingestion pipeline |
 | `src_ip` | string | Source IP (sanitized) |
 | `dst_ip` | string | Destination IP (sanitized) |
@@ -116,10 +123,18 @@ incidents = load_dataset("witfoo/precinct6-cybersecurity-100m", "incidents", spl
 | `vendor_code` | string | Vendor-specific event code |
 | `message_sanitized` | string | Full sanitized raw log message |
 | `label_binary` | string | `malicious`, `suspicious`, or `benign` |
-| `label_confidence` | float | Confidence score (0.0–1.0) |
-| `suspicion_score` | float | WitFoo suspicion score (0.0–1.0) |
+| `label_confidence` | float | Confidence score (0.0–1.0). See [Scoring](#scoring). |
+| `attack_techniques` | string | JSON array of MITRE ATT&CK technique IDs (e.g., `["T1041","T1567"]`) |
+| `attack_tactics` | string | JSON array of MITRE ATT&CK tactic IDs (`TA0001`-style) |
+| `defense_techniques` | string | JSON array of MITRE D3FEND defense technique IDs |
+| `suspicion_score` | float | WitFoo suspicion score (0.0–1.0). See [Scoring](#scoring). |
 | `mo_name` | string | Modus operandi (e.g., `Data Theft`) |
 | `lifecycle_stage` | string | Kill chain stage (e.g., `initial-compromise`, `complete-mission`) |
+| `disposition` | string | Raw Precinct status (`Disrupted`, `Investigating`, `Resolved`, `Dismissed`, `False Positive`, `Unprocessed`) |
+| `disposition_category` | string | Bucketed disposition (`confirmed-malicious`, `false-positive`, `dismissed`, `automated`) |
+| `is_false_positive` | bool | True if SOC analyst marked the incident as a false positive |
+| `status_name` | string | Same as `disposition` (raw Precinct status) |
+| `incident_ids` | string | JSON array of incident UUIDs (empty for benign/suspicious) |
 | `matched_rules` | string | JSON array of matched WitFoo lead rule descriptions |
 | `set_roles` | string | JSON array of classification roles (e.g., `Exploiting Host`, `C2 Server`) |
 | `product_name` | string | Security product name (e.g., `ASA Firewall`, `Falcon`) |
@@ -144,62 +159,118 @@ The dataset contains events from **158 security products** across **70+ vendors*
 
 **Three-tier labels** derived from two sources:
 
-- **`malicious`** (125,780): Events embedded as leads inside confirmed incidents. Extracted directly from incident lead objects with suspicion scores, modus operandi, and MITRE mappings.
-- **`suspicious`** (622,700): Events matching WitFoo's 261 lead detection rules (e.g., "ASA Deny", "Windows Failed Login", "CrowdStrike Detection") but not in confirmed incidents.
-- **`benign`** (113,326,050): Events not matching any detection rules and not in any incident.
+- **`malicious`** (148,128): Events embedded as leads inside confirmed incidents. Extracted directly from incident lead objects with suspicion scores, modus operandi, and MITRE mappings.
+- **`suspicious`** (62,899): Events matching WitFoo's 261 lead detection rules but not present in confirmed incidents.
+- **`benign`** (41,447,721): Events not matching any detection rules and not in any incident.
 
-The `matched_rules` column shows which detection rules matched. The `set_roles` column shows WitFoo classification roles (Exploiting Host, C2 Server, Staging Target, etc.).
+### Ground Truth and Disposition
 
-## Lead Detection Rules
+**All labels in this dataset are derived from WitFoo Precinct's automated incident correlation engine — there is no independent, analyst-verified ground truth.** Researchers should treat Precinct's analysis as a strong but imperfect oracle. The `disposition` column lets you assess label quality on a per-record basis:
 
-261 rules included in `reference/lead_rules_catalog.json`. Examples:
+| `disposition` | Meaning | Confidence in label |
+|---------------|---------|---------------------|
+| `Disrupted` | SOC analyst confirmed the incident and intervened | High — human-confirmed malicious |
+| `Investigating` | SOC analyst is actively investigating | Medium — analyst engaged |
+| `Resolved` | SOC analyst confirmed and resolved | High — human-confirmed malicious |
+| `Dismissed` | SOC analyst dismissed the incident | Negative — analyst rejected |
+| `False Positive` | SOC analyst confirmed false positive | Negative — analyst rejected |
+| `Unprocessed` | Automated detection, no human review | Lower — Precinct-confidence only |
 
-| Rule | Criteria | Roles |
-|------|----------|-------|
-| Blocked Action | Any firewall block | Exploiting Host → Exploiting Target |
-| ASA Deny | cisco_asa + deny | Exploiting Host → Exploiting Target |
-| Windows Failed Login | Event ID 4625 | Exploiting Target → Exploiting Host |
-| CrowdStrike Detection | CrowdStrike stream | Exploiting Target → Exploiting Host |
-| AWS VPC Reject | VPC flow + REJECT | Exploiting Host → Exploiting Target |
-| Authentication Failure | auth_failure type | Exploiting Host → Exploiting Target |
-| Audit log cleared | Event ID 1102 | Exploiting Target → Exploiting Host |
+The `disposition_category` column buckets these into four values for easier filtering: `confirmed-malicious`, `false-positive`, `dismissed`, `automated`. For experiments where ground-truth quality matters, restrict to records where `disposition` ∈ {`Disrupted`, `Resolved`} to compare against analyst-confirmed labels. **This dataset has 82,782 records with `Disrupted` status, 20 with `Dismissed`, and the remainder with `Unprocessed` (no analyst review).**
 
-## Sanitization
+For benign and suspicious records, `disposition` is `Unprocessed` (no incident association). For malicious records, `disposition` reflects the parent incident's status at extraction time.
+
+### Scoring
+
+The dataset exposes two related score fields:
+
+- **`suspicion_score`** (float, 0.0–1.0) — Precinct's proprietary suspicion score from the parent incident. Populated for malicious records; zero for benign and suspicious. (Previously this column was always `0` — that was a defaulting bug, now fixed.)
 
-All customer-identifying information has been removed through a comprehensive, iterative four-layer sanitization pipeline. Quality was prioritized over processing speed. The pipeline is [open source](https://github.com/witfoo/dataset-from-precinct6) under the Apache 2.0 license.
+- **`label_confidence`** (float, 0.0–1.0) — Confidence in the assigned `label_binary` tier. Computed deterministically from corroborating signal:
 
-1. **Structured field sanitization + Aho-Corasick multi-pattern sweep** — Known fields are replaced with deterministic tokens (IPs to [RFC 5737](https://datatracker.ietf.org/doc/html/rfc5737) ranges, hostnames to `HOST-NNNN`, etc.). Every record is then swept using an [Aho-Corasick automaton](https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm) built from all 302,000+ registry entries to catch PII in unexpected contexts. Product identifiers are explicitly protected from the sweep.
-2. **Format-specific log message parsing** — Eight specialized parsers handle Cisco ASA syslog, Windows Security Event XML, WinLogBeat JSON, AWS CloudTrail, Palo Alto Networks, VMware vCenter, DNS logs, and a generic fallback — sanitizing PII within structured fields like XML elements, nested JSON, and CSV columns.
-3. **Machine learning residual detection** — [Microsoft Presidio](https://microsoft.github.io/presidio/) (spaCy NLP) and [BERT NER](https://huggingface.co/dslim/bert-base-NER) scan sanitized records for residual PII that pattern-based approaches miss. New discoveries trigger full re-sanitization.
-4. **Large language model contextual review** — [Claude](https://www.anthropic.com/claude) reviews stratified samples for subtle PII: embedded org names, internal hostnames revealing organizational structure, employee names in file paths, AD group names, and geographic identifiers. Findings trigger re-sanitization.
+  | Label | Formula |
+  |-------|---------|
+  | `malicious` | `max(0.6, suspicion_score)` clamped to 0.95; lowered to 0.3 if `is_false_positive` |
+  | `suspicious` | `0.4 + 0.1 × n_matched_rules + 0.05 × n_set_roles`, clamped to [0.5, 0.85] |
+  | `benign` | `0.5` (no positive evidence either way) |
 
-**Iterative convergence:** The four layers run in cycles. PII discovered by ML/AI in one cycle is caught automatically by Layer 1 in all subsequent cycles across the complete dataset. Cycles repeat until near-zero new discoveries.
+  Note: `label_confidence` is **not** the probability the activity is malicious — it indicates how much corroborating evidence supports the assigned tier. Source: [`src/precinct6_dataset/label.py`](https://github.com/witfoo/dataset-from-precinct6/blob/main/src/precinct6_dataset/label.py).
 
-**Final PII registry:** ~302,000 unique mappings across 14 categories (IPs, hostnames, usernames, orgs, credentials, SIDs, emails, ARNs, etc.). All replacements are consistent — the same original value always maps to the same token, preserving graph topology.
+### MITRE ATT&CK Mappings
+
+Attack technique and tactic labels are derived from three sources, with deduplication:
+
+1. **WitFoo set role names** attached to the incident (e.g., `C2 Server` → `TA0011` Command and Control, `T1071` Application Layer Protocol)
+2. **Modus operandi** name on the incident (e.g., `Ransomware` → `TA0001`, `TA0002`, `TA0040`; `T1486` Data Encrypted for Impact)
+3. **Per-product framework data** embedded in `incident.nodes.products.frameworks` (when present)
+
+Tactic IDs use the standard MITRE ATT&CK Enterprise format (`TA0001` through `TA0043`). Technique IDs are top-level techniques representing the most likely category for a given role. Researchers wanting precise per-event technique attribution should treat these as priors. Full mapping tables in [`src/precinct6_dataset/mitre_mapping.py`](https://github.com/witfoo/dataset-from-precinct6/blob/main/src/precinct6_dataset/mitre_mapping.py).
+
+**Per-edge/per-node attribution in graph output:** `attack_tactics`, `attack_techniques`, `set_roles`, `lifecycle_stage`, `label_binary`, `label_confidence`, `suspicion_score`, `disposition` are attached at the **edge** level in `edges.jsonl` and in per-incident GraphML files. Nodes in `incidents.jsonl` carry their own `sets` and `products` dicts with per-entity information.
 
 ## Graph Data
 
 | Component | Count |
 |-----------|-------|
-| Nodes | 23,362 |
-| Edges | 32,732,650 |
-| Incidents | 10,442 (Data Theft: 10,441, Phishing: 1) |
+| Nodes (artifact-derived hosts + credentials) | 44,717 |
+| Edges (artifact `EVENT`/`NETWORK_FLOW` + incident `INCIDENT_LINK`) | 9,494,858 |
+| Incidents | 12,361 |
+
+Per-edge labels in `edges.jsonl` include `attack_techniques`, `attack_tactics`, `set_roles`, `lifecycle_stage`, `disposition`, `mo_name`, `suspicion_score`, `incident_id` (for INCIDENT_LINK edges).
+
+## Attack Reports
+
+`graph/attack_reports.jsonl` contains one natural-language threat-hunting report per incident (**12,361 reports**). Each report is deterministically composed from structured incident metadata (modus operandi, set roles, lead descriptions, MITRE mappings, timestamps) and explicitly states that it **reflects Precinct's automated correlation engine output, not an independent threat-hunting investigation**.
+
+Each record contains: `incident_id`, `report_text`, `mo_name`, `suspicion_score`, `disposition`, `attack_techniques`, `attack_tactics`, `lead_count`, `set_role_names`, `matched_rules`, `products_observed`, `lifecycle_stage`, and timing fields.
+
+Researchers can audit exactly how each sentence is derived by reading [`src/precinct6_dataset/attack_reports.py`](https://github.com/witfoo/dataset-from-precinct6/blob/main/src/precinct6_dataset/attack_reports.py).
+
+## Additional Files
+
+- **`signals/signals-NNNNN.parquet`** — 21 Parquet shards (~280 MB each), 2M rows per shard except final shard. Total ~5.9 GB.
+- **`signals/metadata.json`** — Per-shard summary, label/disposition distributions, top message types and streams.
+- **`graph/nodes.jsonl`** — 44,717 graph nodes (hosts + credentials derived from artifact src/dst/username).
+- **`graph/edges.jsonl`** — 9,494,858 graph edges, each with `labels` dict carrying MITRE, disposition, set_roles, etc.
+- **`graph/incidents.jsonl`** — Full incident records (12,361 lines, ~638 MB) with embedded `nodes`, `edges`, `leads`, and framework mappings.
+- **`graph/incidents_graphml/{0-f}/{incident_id}.graphml`** — **12,361 per-incident GraphML files**, sharded into 16 subdirectories by first hex char of the incident UUID (HuggingFace caps directories at 10,000 files). Each file is small (KB-MB) and loadable in Gephi, NetworkX, igraph, or DGL. Ideal for graph-based research where loading the entire dataset is impractical.
+- **`graph/attack_reports.jsonl`** — Natural-language threat-hunting reports (12,361 reports, ~21 MB). See [Attack Reports](#attack-reports).
+- **`reference/lead_rules_catalog.json`** — Complete catalog of 261 WitFoo lead detection rules, 158 security products, 106 classification sets, and 216 stream-to-product mappings.
+
+**Note on the artifact-level graph:** This 41.6M-row extraction yielded fewer events than the previously-published 114M version — the difference reflects Cassandra coordinator behaviour during full-partition extraction. Methodology and content fields are otherwise identical to the 2M version.
+
+## Sanitization
+
+All customer-identifying information has been removed through a comprehensive 4-layer sanitization pipeline. The pipeline is [open source](https://github.com/witfoo/dataset-from-precinct6) under the Apache 2.0 license.
+
+1. **Structured field sanitization + Aho-Corasick multi-pattern sweep** — Known fields are replaced with deterministic tokens (IPs → [RFC 5737](https://datatracker.ietf.org/doc/html/rfc5737) ranges, hostnames → `HOST-NNNN`, etc.). Every record is then swept using an Aho-Corasick automaton built from 97,000+ PII registry entries.
+2. **Format-specific log message parsing** — Eight specialized parsers handle Cisco ASA syslog, Windows Security Event XML, WinLogBeat JSON, AWS CloudTrail, Palo Alto Networks, VMware vCenter, DNS logs, and a generic fallback.
+3. **Machine learning residual detection** — [Microsoft Presidio](https://microsoft.github.io/presidio/) (spaCy NLP) and [BERT NER](https://huggingface.co/dslim/bert-base-NER) scan for residual PII. New discoveries trigger full re-sanitization.
+4. **Large language model contextual review** — [Claude](https://www.anthropic.com/claude) reviews stratified samples for subtle PII. Findings trigger re-sanitization.
+
+The four layers run in cycles. PII discovered by ML/AI in one cycle is caught automatically by Layer 1 in all subsequent cycles. **Final PII registry: 97,046 unique mappings across 13 categories** (IPs, hostnames, usernames, orgs, credentials, SIDs, emails, ARNs, etc.). All replacements are consistent — the same original value always maps to the same token, preserving graph topology.
+
+## Research Context
+
+This dataset was produced in collaboration with the University of Canterbury (New Zealand) Computer Science and Software Engineering department for two research projects:
+- **AI Cyber-Security Battle Simulator** — Improving CybORG with realistic IDS observations, graph-based defense policies, and AI-driven attacker modeling
+- **Intrusion Detection based on Provenance Graphs** — Evaluating reproducibility and generalizability of KnowHow and NodLink detection methods
 
 ## Limitations
 
-- **Label imbalance**: 99.77% benign reflects production SOC reality. Sampling strategies needed for balanced training.
-- **Temporal scope**: July–August 2024
-- **Sanitization**: ~302,000 PII registry entries used for consistent replacement across all records
-- **Shared incidents**: Same 10,442 incidents appear in both 2M and 114M datasets
-- **Sanitization trade-offs**: Some log message detail reduced by PII replacement
+- **Label imbalance**: 99.49% benign reflects production SOC reality. Sampling strategies needed for balanced training.
+- **Temporal scope**: July–August 2024.
+- **Ground truth**: All labels derive from Precinct's automated correlation. Use the `disposition` column to stratify by analyst review level.
+- **Shared incidents**: The same 12,361 incidents appear in both the 2M and this dataset (incidents are stored separately from signal data; only the signal sample size differs).
+- **Sanitization trade-offs**: Some log message detail is reduced by PII replacement, particularly in free-text fields.
 
 ## Citation
 
 ```bibtex
-@dataset{witfoo_precinct6_100m_2025,
-  title={WitFoo Precinct6 Cybersecurity Dataset (114M)},
+@dataset{witfoo_precinct6_100m_2026,
+  title={WitFoo Precinct6 Cybersecurity Dataset (large)},
   author={WitFoo, Inc.},
-  year={2025},
+  year={2026},
   url={https://huggingface.co/datasets/witfoo/precinct6-cybersecurity-100m},
   license={Apache-2.0}
 }

graph/{graph.graphml → attack_reports.jsonl}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:883570ee21dfc041d56f8e7a52de46eb251585b9ca956bb1f7d3393c73248114
-size 22139401
+oid sha256:580f5c2e77fabd4c4169179d06cc18dd9539ea2ef78915c120647d54bf08cdb4
+size 21888398

graph/graph.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5edb37a47134f03488762eb6883d856e53147733672c825079e6d30c0b03cb9f
-size 18970745

graph/metadata.json

@@ -1,17 +0,0 @@
-{
-  "dataset_name": "WitFoo Precinct6 Cybersecurity Dataset",
-  "version": "1.0.0",
-  "node_count": 23362,
-  "edge_count": 32732650,
-  "label_distribution": {
-    "benign": 31995325,
-    "malicious": 125700,
-    "suspicious": 611625
-  },
-  "edge_type_distribution": {
-    "EVENT": 32193601,
-    "NETWORK_FLOW": 505035,
-    "DNS_RESOLVE": 34014
-  },
-  "incident_count": 10442
-}

signals/metadata.json

@@ -1 +1,154 @@
-{"row_count": 114074530, "shard_count": 58, "refresh_trigger": true}
+{
+  "total_records": 41658748,
+  "n_shards": 21,
+  "total_size_bytes": 6285096828,
+  "label_distribution": {
+    "benign": 41447721,
+    "suspicious": 62899,
+    "malicious": 148128
+  },
+  "disposition_distribution": {
+    "Unprocessed": 41575946,
+    "Disrupted": 82782,
+    "Dismissed": 20
+  },
+  "top_message_types": {
+    "security_audit_event": 9229251,
+    "4662": 2812876,
+    "5156": 2432714,
+    "audit_log": 2118371,
+    "4658": 2024740,
+    "AssumeRole": 1746338,
+    "DescribeInstanceStatus": 1585360,
+    "unknown": 1559702,
+    "GetLogEvents": 1256453,
+    "4656": 1014502,
+    "4690": 1013158,
+    "DescribeInstances": 794197,
+    "DescribeLoadBalancers": 689914,
+    "ListClusters": 499015,
+    "systemd_event": 458497,
+    "DescribeAccountAttributes": 432371,
+    "GetBucketAcl": 307794,
+    "ListFunctions20150331": 301350,
+    "UpdateInstanceInformation": 285744,
+    "diagnostic_event": 240546,
+    "communication": 233061,
+    "DescribeVolumes": 232224,
+    "pam_event": 227289,
+    "DescribeClusters": 221341,
+    "GetBucketLocation": 218321,
+    "DescribeVpcs": 218177,
+    "GetBucketMetricsConfiguration": 210779,
+    "account_logon": 206951,
+    "DescribeTargetGroups": 206590,
+    "ListStacks": 202445,
+    "GetResources": 201427,
+    "account_logoff": 199416,
+    "DescribeSubnets": 190895,
+    "UpdateInstanceAssociationStatus": 185677,
+    "DescribeAlarmHistory": 184829,
+    "GetSendQuota": 178419,
+    "DescribeAccountLimits": 177594,
+    "4776": 170092,
+    "ListTopics": 168937,
+    "DescribeLimits": 162647,
+    "ListTables": 156659,
+    "access_log": 146831,
+    "5158": 143544,
+    "18453": 141318,
+    "DescribeAddresses": 140900,
+    "DescribeAutoScalingGroups": 139013,
+    "DescribeImages": 135742,
+    "GenerateDataKey": 135246,
+    "DescribeInternetGateways": 131041,
+    "auth_event": 126943
+  },
+  "top_streams": {
+    "aws_cloudtrail_events": 16087719,
+    "microsoft-windows-security-auditing": 10481332,
+    "windows_security_audit": 9229251,
+    "aws_cloud_trail": 1994628,
+    "no_useful_info": 1503924,
+    "crond": 432131,
+    "sshd": 248142,
+    "filebeat_diagnostic": 240546,
+    "network_communication": 233061,
+    "pam": 227289,
+    "mssql$paygsql01": 193458,
+    "systemd_logind": 126943,
+    "cisco_asa": 99488,
+    "linux_audit": 90726,
+    "aws_vpc_flow_log": 58162,
+    "unknown": 55441,
+    "security": 53331,
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