T0887 Wireless Sniffing — Attack Simulation

Complete technique description

Adversaries may seek to capture radio frequency (RF) communication used for remote control and reporting in distributed environments. RF communication frequencies vary between 3 kHz to 300 GHz, although are commonly between 300 MHz to 6 GHz. [Candell, R., Hany, M., Lee, K. B., Liu,Y., Quimby, J., Remley, K. April 2018] The wavelength and frequency of the signal affect how the signal propagates through open air, obstacles (e.g. walls and trees) and the type of radio required to capture them. These characteristics are often standardized in the protocol and hardware and may have an effect on how the signal is captured. Some examples of wireless protocols that may be found in cyber-physical environments are: WirelessHART, Zigbee, WIA-FA, and 700 MHz Public Safety Spectrum.

Adversaries may capture RF communications by using specialized hardware, such as software defined radio (SDR), handheld radio, or a computer with radio demodulator tuned to the communication frequency. [Bastille April 2017] Information transmitted over a wireless medium may be captured in-transit whether the sniffing device is the intended destination or not. This technique may be particularly useful to an adversary when the communications are not encrypted. [Gallagher, S. April 2017]

In the 2017 Dallas Siren incident, it is suspected that adversaries likely captured wireless command message broadcasts on a 700 MHz frequency during a regular test of the system. These messages were later replayed to trigger the alarm systems. [Gallagher, S. April 2017]

Official technique definition · Open in the interactive matrix

Detection telemetry and configuration

These contracts are selected through exact technique-to-data-component relationships or explicitly labelled planning overrides. Configure a collector at the relevant observation point; a telemetry name alone is not coverage.

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0078",
  "collector": "illustrative-lab-collector",
  "observation": {
    "source_ip": "192.0.2.10",
    "destination_ip": "198.51.100.20",
    "destination_port": 443,
    "protocol": "tcp",
    "bytes_sent": 128,
    "bytes_received": 512
  }
}

{
  "schema": "1200km.telemetry.example.v1",
  "synthetic": true,
  "timestamp": "2026-09-27T12:00:00Z",
  "telemetry_id": "DC0034",
  "collector": "illustrative-lab-collector",
  "observation": {
    "process_id": 4200,
    "image": "/usr/bin/sleep",
    "owner_uid": 1000,
    "started_at": "2026-09-27T11:59:00Z"
  }
}

Detection rules and analytic guidance

Open the dedicated detection workspace · Existing query engineering

Before treating a rule as coverage

1. Read the actual condition and logsource; identify attacker-side, victim-side or third-party visibility.
2. Map native sensor fields, parse times and identities, then compile for the selected backend.
3. Replay a behavior-positive case, a normal control, an authorized look-alike and a missing-telemetry case.
4. Record matches and misses with rule version and source events. No match is inconclusive when required telemetry is absent.

Anomaly-based detection

No exact technique-specific Atlas model is present for T0887 in the selected source snapshot. The following is a design worksheet, not a newly validated model.

Anomaly engineering worksheet

Start from the exact analytic above. The collection-specific lenses below are analyst recommendations; they do not assert that an arbitrary statistical deviation detects this technique.

• Network and protocol telemetry: Per source and observation point: connection rate, destination-host/port fan-out, response/failure ratio and previously unseen destinations. Compare equivalent time windows; separate authorized scanners, NAT and sampling effects.
• Host state and inventory: Compare authorized state snapshots: first-seen drivers/services/modules, unexpected configuration drift and integrity changes. Record collection cadence; absence in a sample is not evidence of deletion.

1. Define an entity/peer group and feature; exclude the evaluation period from baseline training.
2. Use representative normal and maintenance activity. Do not invent a production threshold from a synthetic example.
3. Evaluate held-out behavior-positive and benign cases; measure false positives, misses and telemetry loss separately.
4. Keep abnormality separate from maliciousness. Correlate with the behavior-specific source evidence.

Anomaly Detection Atlas · Baseline, validation and blind spots

Attack tools and documented software

No reviewed lab-tool association is selected for this exact technique. This is a curation gap, not a statement that no tool can implement the behavior.

Simulation design and documented procedures

Atomic evidence: no compatible candidate in this snapshot. Live lab status: not run for these imported procedures.

Use a process simulator or isolated test rig with no connection to production controllers or physical actuators. Preserve safety interlocks; destructive effects require synthetic evidence, not real equipment damage.

No compatible Atomic procedure is present in this pinned evidence set. Technical feasibility has not been ruled out. A custom procedure must define the behavior, target, collection point, success observable and rollback before execution.

Technique-specific experiment contract

1. Choose one procedure implementing T0887 Wireless Sniffing; identify the observable behavior in the source definition, not just its tactic.
2. Record exact target, tool/version, inputs, privileges, network limits and approval. Never run an imported default target automatically.
3. Enable the collection sources above and verify a benign baseline reaches the detector.
4. Run only the approved isolated scenario; capture native events and an independent behavior observable.
5. Evaluate the exact rule/model, restore the lab, and preserve configuration, event hashes, alerts, controls and misses.

Synthetic logs and offline contract check

Download synthetic collection fixture (JSON) · Download the complete engineering workbook

This fixture exercises the listed collection fields. It does not reproduce Wireless Sniffing, label its example events as malicious, or measure detection effectiveness. Use the exact analytic and selected procedure to build an independently labelled behavior-positive dataset.

Not run in this browser. No network scan or attack command is executed by this control.

Validation status and remaining work

Source identity

Joined by exact technique/object IDs against hash-pinned sources.

Collection fixture

Available for offline schema exercises; not attack emulation.

Native telemetry / backend / real attack

Not validated for these imported procedures. Source fidelity and page tests cannot establish sensor or detector effectiveness.

All-page audit and evidence inventory · This page’s machine-readable workbook

Sources and evidence boundaries

Pinned MITRE STIX source · SHA-256 c9ca0ce523a1b5e512060a390415e8aefe8f6d32c5f82d7de81264960c1e1183. Definition and procedure context are reproduced from this snapshot, not independently re-investigated incidents.

• mitre-attack
• Bastille April 2017
• Candell, R., Hany, M., Lee, K. B., Liu,Y., Quimby, J., Remley, K. April 2018
• Gallagher, S. April 2017