README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - cybersecurity
  - identity-security
  - account-takeover
  - mfa-bypass
  - ueba
  - zero-trust
  - apt
  - synthetic-data
  - lateral-movement
  - golden-ticket
  - time-series
  - format:tsfile
pretty_name: CYB006 — Synthetic Login Activity Dataset (Sample) (TsFile)
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: attack_timelines
        path: attack_timelines.tsfile
      - split: auth_events
        path: auth_events.tsfile
      - split: campaign_events
        path: campaign_events.tsfile
      - split: campaign_summary
        path: campaign_summary.tsfile
      - split: identity_topology
        path: identity_topology.tsfile
      - split: login_sessions
        path: login_sessions.tsfile
      - split: user_risk_summary
        path: user_risk_summary.tsfile
      - split: victim_topology
        path: victim_topology.tsfile

CYB006 — Synthetic Login Activity Dataset (Sample) (TsFile)

Apache TsFile version of xpertsystems/cyb006-sample.

Overview

CYB006 — Synthetic Login Activity Dataset (Sample) — This is a free preview of the full CYB006 — Synthetic Login Activity Dataset product. It contains roughly ~1.3% of the full dataset at identical schema, threat-actor-tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

• Tables: attack_timelines, auth_events, campaign_events, campaign_summary, identity_topology, login_sessions, user_risk_summary, victim_topology
• Rows: 265,676 across 8 TsFile table(s).
  • attack_timelines: 37,489 rows — Time is the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
  • auth_events: 31,900 rows — Time is the source event_timestamp_utc second counter on the simulation clock, converted to milliseconds.
  • campaign_events: 190,137 rows — Time is the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
  • campaign_summary: 500 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
  • identity_topology: 150 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
  • login_sessions: 5,000 rows — Time is the source session_timestamp_utc second counter on the simulation clock, converted to milliseconds.
  • user_risk_summary: 200 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
  • victim_topology: 300 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.

Schema (TsFile structure)

attack_timelines.tsfile

• Time (INT64, milliseconds) — the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
• TAG: campaign_id — device dimension(s); query one device with WHERE campaign_id=....
• FIELD (19): actor_id (string), attack_phase (string), files_encrypted_cumulative (int64), encryption_throughput_mbps (double), endpoints_compromised (int64), lateral_move_count (int64), credential_harvest_count (int64), c2_bytes_exfiltrated (double), defender_alert_score (double), detection_outcome (string), blast_radius_pct (double), actor_capability_tier (string), living_off_land_score (double), attribution_risk_score (double), data_exfiltrated_gb (double), wiper_flag (int64), double_extortion_flag (int64), ir_activated (int64), target_segment_id (string)

auth_events.tsfile

• Time (INT64, milliseconds) — the source event_timestamp_utc second counter on the simulation clock, converted to milliseconds.
• TAG: user_id — device dimension(s); query one device with WHERE user_id=....
• FIELD (8): event_id (string), session_id (string), event_type (string), target_account_id (string), source_domain_id (string), target_domain_id (string), application_id (string), risk_score_delta (double)

campaign_events.tsfile

• Time (INT64, milliseconds) — the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
• TAG: campaign_id — device dimension(s); query one device with WHERE campaign_id=....
• FIELD (6): actor_id (string), event_type (string), target_segment_id (string), defender_id (string), affected_endpoint_id (string), event_detail (string)

campaign_summary.tsfile

• Time (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
• TAG: campaign_id — device dimension(s); query one device with WHERE campaign_id=....
• FIELD (42): actor_id (string), actor_capability_tier (string), ransomware_family (string), operator_model (string), victim_industry_vertical (string), target_segment_id (string), backup_maturity_tier (string), soc_maturity_tier (string), edr_coverage_rate (double), network_segmentation_quality (double), total_dwell_time_hrs (double), endpoints_encrypted_total (int64), blast_radius_pct (double), files_encrypted_total (int64), data_exfiltrated_gb (double), ransom_demand_usd (double), ransom_paid_flag (int64), recovery_time_hrs (double), total_business_downtime_hrs (double), ir_containment_speed_score (double), backup_recovery_success_flag (int64), double_extortion_flag (int64), wiper_component_flag (int64), lateral_move_count_total (int64), credential_harvest_count_total (int64), detection_phase (string), campaign_success_flag (int64), financial_impact_score (double), resilience_score (double), mean_time_to_detect_hrs (double), mean_time_to_contain_hrs (double), edr_alerts_total (int64), siem_fires_total (int64), vss_deletion_success (int64), backup_destruction (int64), worm_spread_flag (int64), coordinated_flag (int64), living_off_land_score (double), lateral_coverage_ratio (double), encrypted_data_recoverability (double), insurance_loss_ratio_score (double), attack_efficiency_score (double)

identity_topology.tsfile

• Time (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
• TAG: domain_id — device dimension(s); query one device with WHERE domain_id=....
• FIELD (13): application_id (string), domain_type (string), trust_relationship_type (string), mfa_enforcement_policy (string), conditional_access_strength (double), user_count (int64), privileged_account_ratio (double), service_account_count (int64), ueba_coverage_level (double), directory_sync_enabled (int64), sso_provider (string), session_timeout_seconds (int64), password_policy_strength (double)

login_sessions.tsfile

• Time (INT64, milliseconds) — the source session_timestamp_utc second counter on the simulation clock, converted to milliseconds.
• TAG: user_id — device dimension(s); query one device with WHERE user_id=....
• FIELD (20): session_id (string), session_phase (string), login_outcome (string), source_ip_hash (string), geo_country_code (string), geo_city_hash (string), device_id_hash (string), device_trust_level (string), authentication_method (string), mfa_challenge_type (string), mfa_response_latency_ms (double), credential_attempt_count (int64), session_duration_seconds (int64), target_application_id (string), privilege_level_accessed (string), user_risk_tier (string), threat_actor_capability_tier (string), geo_anomaly_score (double), velocity_anomaly_score (double), impossible_travel_flag (int64)

user_risk_summary.tsfile

• Time (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
• TAG: user_id — device dimension(s); query one device with WHERE user_id=....
• FIELD (19): user_risk_tier (string), total_login_attempts (int64), successful_logins (int64), failed_logins (int64), mfa_failures (int64), impossible_travel_events (int64), lateral_hop_count (int64), privilege_escalations (int64), account_lockout_count (int64), geo_dispersion_score (double), login_velocity_score (double), session_anomaly_rate (double), ueba_alert_count (int64), threat_actor_flag (int64), account_takeover_flag (int64), overall_identity_risk_score (double), peak_privilege_level_accessed (string), credential_attack_victim_flag (int64), insider_threat_indicator_score (double)

victim_topology.tsfile

• Time (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
• TAG: segment_id — device dimension(s); query one device with WHERE segment_id=....
• FIELD (14): defender_id (string), segment_type (string), soc_maturity_tier (string), backup_maturity_tier (string), edr_coverage_rate (double), network_segmentation_quality (double), patch_posture_score (double), ir_activation_latency_hrs (double), endpoint_count (int64), ad_domain_complexity (double), soc_maturity_score (double), backup_recovery_prob (double), backup_recovery_hrs_mean (double), siem_rule_refresh_cadence_days (int64)

Conversion notes

• Each CSV of the sample is converted into its own TsFile table (8 tables), so no table is merged with another and no column is dropped.
• Event/telemetry tables use the source simulation clock as Time (INT64 milliseconds): timestep step counters and millisecond offsets are stored as-is, minute-based columns are multiplied by 60,000, and ISO/UTC strings are parsed to epoch milliseconds.
• Static inventory/topology/summary tables have no per-row clock; their Time is a synthetic row index (0..N-1) over the stable source order.
• Low-cardinality dimensions of each table (campaign/incident/asset/session ids, phases, labels) are declared as TAG columns; query one simulation with e.g. WHERE campaign_id='...'.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("attack_timelines.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

• Original dataset: xpertsystems/cyb006-sample
• Source card title / publisher: CYB006 — Synthetic Login Activity Dataset (Sample)
• License: cc-by-nc-4.0 (as declared by the original dataset).