Abstract: A device may receive event data identifying events associated with user devices utilizing a video conferencing system, and may utilize the event data to train one or more fraud detection models. The device may receive, from a user device, user traffic including at least one of API traffic or media traffic, and may identify characteristics of the user traffic, such as activity related features, application-specific features, and dial-in number-specific features. The device may process at least one of the activity related features, the application-specific features, or the dial-in number-specific features, with the one or more fraud detection models, to determine whether the user traffic from the user device is anomalous or normal, and may block at least one of an IP address of the user device, a telephone number of the user device, or a meeting of the user device, based on determining that the user traffic is anomalous.

Abstract: Disclosed is a system for analyzing and optimizing conference experiences. The system may identify parameters associated with a conference between a set of User Equipment (“UEs”) used by different conference participants to access the conference. The system may compare the identified parameters to sets of parameters associated with a classification model. The system may identify, based on the comparing and the model output, a particular classification for the conference, and may calculate an impact of each conference parameter on the particular classification based on a particular parameter combination included in the model for the particular classification. The system may determine that a subset of the conference parameters has a larger impact on the particular classification than other conference parameters, may select one or more actions that are associated with the particular classification and the subset of parameters, and may perform the one or more actions.