Abstract: Dynamic thresholds are derived for each connection phase, using machine learning (e.g., K-means clustering) for an enterprise network. A time interval can be tracked between samples of collected data packets for each phase of connections, including the association phase, the authentication phase and the DHCP phase of connecting. A specific dynamic threshold for one of the connection phases is detected as out-of-range. Responsive to the out-of-range detection, network issues corresponding to the phase of the specific dynamic threshold are checked and automatically remediated.

Abstract: A baseline multicast traffic is derived for an SSID from the network traffic statistics using unsupervised machine learning. Responsive to detecting a deterioration in the real-time network traffic statistics for the SSID in relation to the baseline throughput and the baseline multicast traffic, the multicast data rate can be adjusted to match the lowest unicast data rate for the SSID.

Abstract: Each of the plurality of stations connected to the access point can be profiled to determine device type, and determine a listen interval for each of the plurality of stations based on the device prioritization model based on DTIM periods of the plurality of stations. Delivery of multicast packets is prioritized from the enterprise network destined for a low power device multicast group on the Wi-Fi network and to prioritize delivery of unicast packets for low power device multicast group. The messages are transmitted to the stations over the Wi-Fi network according to the assigned listen interval.

Abstract: Each of the plurality of stations connected to the access point can be profiled to determine device type, and determine a listen interval for each of the plurality of stations based on the device prioritization model based on DTIM periods of the plurality of stations. Delivery of multicast packets is prioritized from the enterprise network destined for a low power device multicast group on the Wi-Fi network and to prioritize delivery of unicast packets for low power device multicast group. The messages are transmitted to the stations over the Wi-Fi network according to the assigned listen interval.

Abstract: Multi-level machine learning models can be generated from the captured log events. Outcomes are predicted for input events in real-time. The captured log events are received and parsed to expose event outcome data. A first data set is generated by determining whether an outcome associated with the event outcome data was a success or a failure. Responsive to a failed event outcome, a second data set is generated by categorizing the failed event outcome, to train multiple level SVMs for prediction of Wi-Fi input events and automatic remediation of Wi-Fi issues.

Abstract: Multi-level machine learning models can be generated from the captured log events. Outcomes are predicted for input events in real-time. The captured log events are received and parsed to expose event outcome data. A first data set is generated by determining whether an outcome associated with the event outcome data was a success or a failure. Responsive to a failed event outcome, a second data set is generated by categorizing the failed event outcome, to train multiple level SVMs for prediction of Wi-Fi input events and automatic remediation of Wi-Fi issues.