This article discusses Cato CTRL’s innovative approach to enhancing IoT/OT threat detection through advanced behavioral analysis. The methodology enables the identification of novel threats and has proven effective in automatically detecting and validating new Indicators of Compromise (IoCs). It is scalable, device-agnostic, and integrates seamlessly with existing security infrastructures to protect against emerging threats. Affected: IoT devices, OT devices, security environments
Keypoints :
- Cato CTRL utilizes advanced behavioral analysis for IoT/OT threat detection.
- The detection methodology generates high-confidence IoCs without needing pre-existing threat intelligence.
- New IoCs are identified weekly and validated against threat intelligence sources.
- The device-agnostic approach is highly scalable across various environments.
- The methodology includes a comprehensive traffic monitoring and active threat prevention process.
- Behavioral baselines are established using AI and ML to analyze device characteristics.
- An automatic validation process cross-references findings against established threat intelligence platforms.
- A real-world case highlights the effectiveness of the methodology in detecting anomalies in Yealink VoIP devices.
MITRE Techniques :
- TA0001 – Initial Access: Leveraged through anomalous communications from IoT/OT devices.
- TA0040 – Influence Operations: Detected through traffic anomalies involving communication with previously unseen countries.
- TA0022 – Resource Development: Utilized during the analysis of network patterns of devices.
- TA0042 – Reconnaissance: Conducted by establishing behavioral baselines and peer group comparisons.
Indicator of Compromise :
- No IoC Found
Full Story: https://www.catonetworks.com/blog/cato-ctrl-advanced-behavioral-analysis-iot-ot-devices-ioc-collection/