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Threat Detection

SECKIOT identifies anomalies through its hybrid behavioural analysis technology based on Machine Learning (Artificial Intelligence) and detection rules to detect threat clues and deviant behaviours faster, with minimal false positives. The internal signature database is regularly updated via our threat intelligence in order to guard against any attack or malicious behaviour previously identified among all of SECKIOT's deployed equipment and to avoid new attempts at the same type of attack.

The XIoT & XIoT security-specific use cases developed by our experts facilitate the continuous analysis of network traffic for anomalies and intrusion attempts, including intrusion clues based on MITRE ATT&CK ICS techniques; behaviours indicating the presence of known malware such as Triton/Snake; policy violations; signs of device failure; and deviant machine-to-machine (M2M) communications and behaviours.

With a visual representation based on the MITRE ATT&CK ICS matrix of intrusion attempts targeting your most critical IoT & IOT assets, contextual alerts are fed back into our Alerting Dashboard to give your cyber teams the detail they need to quickly investigate potential threats and prioritise the remediation required to reduce the attack surface.

Our features 

For know threats

+ Indicator of Compromise & Signatures

For unknown threats

+ Behavioural analysis 

Artificial Intelligence: Time-related Machine Learning & Graph Analysis of Network Communications

Alerts are explained and contextualized, they are also classified with the MITRE ATT&CK ICS matrix.

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