Datasets:

THULab
/

cyb003_sample

license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - tsfile
  - timeseries
  - cybersecurity
  - malware
  - malware-classification
  - threat-intelligence
  - apt
  - ransomware
  - synthetic-data
  - edr
  - sandbox-evasion
  - polymorphic
  - time-series
  - format:tsfile
pretty_name: CYB003 — Synthetic Malware Behaviour & Classification (Sample) (TsFile)
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: environment_profiles
        path: environment_profiles.tsfile
      - split: execution_events
        path: execution_events.tsfile
      - split: malware_samples
        path: malware_samples.tsfile
      - split: sample_summary
        path: sample_summary.tsfile

CYB003 — Synthetic Malware Behaviour & Classification (Sample) (TsFile)

Apache TsFile version of xpertsystems/cyb003-sample.

Overview

CYB003 — Synthetic Malware Behaviour & Classification Dataset (Sample) — This is a free preview of the full CYB003 — Synthetic Malware Behaviour & Classification Dataset product. It contains roughly 1 / 56th of the full dataset at identical schema, family/tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

Tables: environment_profiles, execution_events, malware_samples, sample_summary
Rows: 7,256 across 4 TsFile table(s).
- environment_profiles: 100 rows — Time is synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
- execution_events: 1,056 rows — Time is the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
- malware_samples: 6,000 rows — Time is the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
- sample_summary: 100 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)

`environment_profiles.tsfile`

Time (INT64, milliseconds) — synthetic row index over the stable source order (0 … N-1); the source table has no per-row clock.
TAG: profile_id — device dimension(s); query one device with WHERE profile_id=....
FIELD (11): segment_id (string), endpoint_os (string), platform_type (string), ep_stack (string), patch_currency_score (double), user_privilege_level (string), network_segment_type (string), sandbox_presence_flag (int64), av_signature_age_days (int64), edr_behavioural_enabled (int64), network_egress_filtered (int64)

`execution_events.tsfile`

Time (INT64, milliseconds) — the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
TAG: sample_id — device dimension(s); query one device with WHERE sample_id=....
FIELD (9): family_id (string), event_type (string), execution_phase (string), target_pid (int64), api_call_name (string), registry_key_path (string), file_path_mutated (string), network_dst_ip_class (string), dns_query_domain_class (string)

`malware_samples.tsfile`

Time (INT64, milliseconds) — the source timestep simulation step counter, stored as milliseconds (1 step = 1 ms).
TAG: sample_id — device dimension(s); query one device with WHERE sample_id=....
FIELD (29): family_id (string), threat_actor_id (string), malware_family (string), threat_actor_tier (string), target_platform (string), execution_phase (string), obfuscation_technique (string), api_call_rate (double), registry_write_count (int64), network_connection_count (int64), process_injection_flag (int64), c2_beacon_interval_sec (double), detection_outcome (string), av_signature_hit_flag (int64), sandbox_evasion_flag (int64), lateral_propagation_count (int64), privilege_escalation_flag (int64), ep_stack (string), pe_entropy_mean (double), pe_entropy_std (double), import_hash_cluster (int64), section_count (int64), packed_section_ratio (double), string_entropy_mean (double), byte_histogram_chi2 (double), code_section_rx_ratio (double), resource_section_entropy (double), suspicious_import_count (int64), packer_detected_flag (int64)

`sample_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: sample_id — device dimension(s); query one device with WHERE sample_id=....
FIELD (28): family_id (string), threat_actor_id (string), malware_family (string), threat_actor_tier (string), target_platform (string), campaign_attribution_cluster (int64), obfuscation_complexity_score (double), total_api_calls (int64), unique_api_calls (int64), registry_mutations_total (int64), files_mutated_count (int64), network_connections_total (int64), lateral_propagation_count (int64), c2_sessions_established (int64), execution_duration_steps (int64), av_detection_flag (int64), edr_detection_flag (int64), sandbox_evasion_success (int64), definitive_detection_flag (int64), privilege_escalated_flag (int64), data_exfiltrated_flag (int64), dwell_time_ratio (double), family_attribution_confidence (double), lotl_technique_count (int64), packer_detected_flag (int64), threat_actor_roi_score (double), coordinated_campaign_flag (int64), campaign_success_flag (int64)

Conversion notes

Each CSV of the sample is converted into its own TsFile table (4 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("environment_profiles.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/cyb003-sample
Source card title / publisher: CYB003 — Synthetic Malware Behaviour & Classification (Sample)
License: cc-by-nc-4.0 (as declared by the original dataset).