Anomaly Detection
Identifying device behavior that deviates from established baselines. Critical for IoT and OT environments where signature-based detection tools have no coverage of proprietary protocols and devices.
What is Anomaly Detection?
Identifying device behavior that deviates from established baselines. Critical for IoT and OT environments where signature-based detection tools have no coverage of proprietary protocols and devices.
Anomaly detection identifies deviations from established patterns of behavior — what a device does, who it talks to, when it communicates, and how much data it moves. In IT environments, signature-based detection tools rely on known threat patterns. In IoT and OT environments, signature-based approaches fail because devices run proprietary protocols, rarely receive updates, and exhibit highly repetitive, predictable behavior that makes behavioral baselines unusually effective.
The challenge is establishing a trustworthy baseline in the first place. A new device introduced to a network may have no historical behavior to compare against. Devices in clinical environments or on production floors may be active only during specific shifts or maintenance windows. A good anomaly detection engine accounts for temporal patterns, communication peers, protocol usage, and data volumes — not just binary "did it connect to an unusual IP" checks.
False positive management is critical in OT and healthcare. A false positive that blocks a PLC mid-cycle or silences a patient monitor alarm can cause operational or clinical harm. The best anomaly detection systems tune alerts to the risk and criticality of the device, escalating high-confidence deviations while suppressing low-confidence noise.
Key Facts
- 65% of IoT attacks begin with behavior that deviates from the device's normal communication pattern
- Proprietary OT protocols like DNP3 and Modbus have no signature-based detection coverage in most SIEMs
- The average dwell time for OT intrusions before detection is over 200 days
- Behavioral anomaly detection catches lateral movement that perimeter tools miss entirely
How ORDR Addresses Anomaly Detection
ORDR establishes behavioral baselines for every device using continuous passive monitoring. When a device deviates — initiating an unusual connection, running unexpected protocols, or communicating with external IPs — ORDR generates a risk-weighted alert and can automatically enforce a quarantine or segmentation policy. Alert sensitivity is tuned per device type to minimize false positives in critical environments.
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