Systems and methods for determining anomalies using machine learning models

A method for isolating network operations processed using a distributed computing environment, the method comprising:

• receiving, by one or more processors, network operation data representing a plurality of network operations processed by a communications network;
• classifying, by the one or more processors and using an isolation forest model and the network operation data, a subset of network operations from among the plurality of network operations as comprising an anomaly;
• determining, by the one or more processors and for each network operation of the subset of network operations, that the anomaly is a positive anomaly, a negative anomaly, or noise;
• and
• generating, by the one or more processors and at a client device, a graphical user interface (GUI) comprising a warning message, the warning message identifying at least one network operation of the subset of network operations as being a negative anomaly.

A non-transitory computer-readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

• receive network operation data representing a plurality of network operations processed by a communications network;
• classify, using an isolation forest model and the network operation data, a subset of network operations from among the plurality of network operations as comprising an anomaly;
• determine, for each network operation of the subset of network operations, that the anomaly is a positive anomaly, a negative anomaly, or noise;
• and
• generate, at a client device, a graphical user interface (GUI) comprising a warning message, the warning message identifying at least one network operation of the subset of network operations as being a negative anomaly.