Method and apparatus for detecting anomalies in mission critical environments using word representation learning

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process for detecting anomalies in mission-critical environments using word representation learning, the process comprising:

• parsing at least one received data set into a text structure;
• isolating a protocol language of the at least one received data set, wherein the protocol language is a standardized pattern for communication over at least one communication protocol;
• generating at least one document from the contents of the received at least one data set, wherein the at least one document includes at least one parsed text structure referencing a unique identifier;
• detecting insights in the at least one generated document, wherein insights are detected in at least one representation having at least one dimension, wherein the representation is mapped to at least one learned hyperspace;
• extracting rules from the detected insights;
• and
• detecting anomalies by applying the extracted rules on patterns for communication over at least one communication protocol.

A system for detecting anomalies in mission-critical environments using word representation learning, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• parse at least one received data set into a text structure;
• isolate a protocol language of the at least one received data set, wherein the protocol language is a standardized pattern for communication over at least one communication protocol;
• generate at least one document from the contents of the received at least one data set, wherein the at least one document includes at least one parsed text structure referencing a unique identifier;
• detect insights in the at least one generated document, wherein insights are detected in at least one representation having at least one dimension, wherein the representation is mapped to at least one learned hyperspace;
• extract rules from the detected insights;
• and
• detect anomalies by applying the extracted rules on patterns for communication over at least one communication protocol.