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. <EOS>