Abstract: Methods, apparatus, and system for generating efficient cross-layer key performance indicators for monitoring, managing and debugging communications networks. An exemplary method embodiment includes the steps of: generating a plurality of different cross-layer key performance indicators (CL-KPIs) from a set of event data records corresponding to a first period of time and a first base protocol, each CL-KPI in said plurality of different CL-KPIs being for a different failure cause scenario; identifying a CL-KPI in the plurality of different CL-KPIs corresponding to the first period of time and the first base protocol having a highest CL-KPI value and determining a most likely failure cause scenario for said first base protocol to be the failure cause scenario associated with the identified CL-KPI having the highest CL-KPI value.

Abstract: Methods, apparatus, and system for generating efficient cross-layer key performance indicators for monitoring, managing and debugging communications networks. An exemplary method embodiment includes the steps of: generating a plurality of different cross-layer key performance indicators (CL-KPIs) from a set of event data records corresponding to a first period of time and a first base protocol, each CL-KPI in said plurality of different CL-KPIs being for a different failure cause scenario; identifying a CL-KPI in the plurality of different CL-KPIs corresponding to the first period of time and the first base protocol having a highest CL-KPI value and determining a most likely failure cause scenario for said first base protocol to be the failure cause scenario associated with the identified CL-KPI having the highest CL-KPI value.

Abstract: Methods and apparatus for implementing and operating malicious transaction detection systems. An exemplary method embodiment includes the steps of: (i) operating, a malicious transaction detection system, to receive communications session establishment data; operating, the malicious transaction detection system, to determine a probability of whether or not the communications session establishment data indicates that the communications session is malicious; and when the determined probability is greater than or equal to a predetermined threshold value determining that a transaction corresponding to the received communications session establishment data is malicious; and when the determined probability is less than the predetermined threshold value determining that the transaction corresponding to the received communications session establishment data is not malicious; and wherein the malicious transaction detection system includes a determination model trained using synthetic communications session data.