Systems and methods of aviation data communication anomaly detection, as in air traffic control surveillance systems

A method of detecting aviation data communication anomalies in a plurality of interconnected aviation data communication devices, the method comprising the steps of:

• generating device status information for at least a portion of the plurality of interconnected aviation data communication devices;
• receiving the device status information at a processor in communication with at least one of the devices of the portion of the plurality of devices;
• operating a variational autoencoder on the processor that is configured for
• receiving the device status information;
• optimizing the received device status information;
• and
• determining or enabling a user to determine whether the device status information qualifies as an anomaly that requires a response;
• comparing, via the processor, the optimized device status information to at least one of non-anomalous device status data or anomalous device status data in a latent space of the variational autoencoder, wherein the latent space includes an n-D point scatter plot, and wherein the further the optimized device status information is from the non-anomalous device status data in the latent space, the greater the likelihood the device status information represents an anomaly, and wherein the latent space includes hidden vector values;
• and
• performing, via the processor, principal component analysis on the hidden vector to allow for the visualization of n-D point clusters in the latent space.

A non-transitory computer-readable storage medium, comprising one or more programs for executing a model of detecting aviation data communication anomalies in a plurality of interconnected aviation data communication devices by use of a variational autoencoder, wherein the model is configured to:

• receive device status information from at least a portion of the plurality of interconnected aviation data communication devices;
• optimize the received device status information by use of the variational autoencoder;
• determine or enable a user to determine whether the device status information qualifies as an anomaly that requires a response;
• compare, via the processor, the optimized device status information to at least one of non-anomalous device status data or anomalous device status data in a latent space of the variational autoencoder;
• wherein the latent space includes an n-D point scatter plot, and wherein the further the optimized device status information is from the non-anomalous device status data in the latent space, the greater the likelihood the device status information represents an anomaly, the latent space including hidden vector values,
• the model further being configured to perform, via the processor, principal component analysis on the hidden vector to allow for the visualization of n-D point clusters in the latent space.