A method for detecting airline network traffic disruptions, the method utilizing manifolds learning to reduce high-dimensionality airline network traffic data into lower dimensional representations, wherein high-dimensionality airline network traffic data is airline network traffic data that requires more than three dimensions to represent, the method comprising: transmitting, by a computer processor of a user computer to a simulation device, a request for abnormal airline network traffic simulation data; receiving, by the computer processor from the simulation device, abnormal airline network traffic simulation data; generating, by the computer processor utilizing a manifolds learning process, simulated abnormal airline network traffic manifolds representation data; transmitting, by the computer processor to a current network traffic computer, a request for current airline network traffic data; receiving, by the computer processor from the current network traffic computer, the current airline network traffic data; generating, by the computer processor, a current airline network traffic dataset; mapping, by the computer processor, the current airline network traffic dataset to the abnormal airline network traffic manifolds representation; determining, by the computer processor, whether a threshold number of data points of the current airline network traffic dataset map to the abnormal airline traffic manifolds representation; and either in response to determining that the threshold number of data points of the current airline network traffic dataset map to the abnormal airline traffic manifolds representation, transmitting, by the computer processor to an operator device, an alarm indicating that a disruption of airline network traffic is imminent; or in response to determining that the threshold number of data points of the current airline network traffic dataset do not map to the abnormal airline traffic manifolds representation, transmitting, by the computer processor to the operator device, an indication of normal airline network traffic. transmitting, by a computer processor of a user computer to a simulation device, a request for abnormal airline network traffic simulation data; receiving, by the computer processor from the simulation device, abnormal airline network traffic simulation data; generating, by the computer processor utilizing a manifolds learning process, simulated abnormal airline network traffic manifolds representation data; transmitting, by the computer processor to a current network traffic computer, a request for current airline network traffic data; receiving, by the computer processor from the current network traffic computer, the current airline network traffic data; generating, by the computer processor, a current airline network traffic dataset; mapping, by the computer processor, the current airline network traffic dataset to the abnormal airline network traffic manifolds representation; determining, by the computer processor, whether a threshold number of data points of the current airline network traffic dataset map to the abnormal airline traffic manifolds representation; and either in response to determining that the threshold number of data points of the current airline network traffic dataset map to the abnormal airline traffic manifolds representation, transmitting, by the computer processor to an operator device, an alarm indicating that a disruption of airline network traffic is imminent; or in response to determining that the threshold number of data points of the current airline network traffic dataset do not map to the abnormal airline traffic manifolds representation, transmitting, by the computer processor to the operator device, an indication of normal airline network traffic.