Syniverse Technologies, LLC

Ramakanth Vallur

Detecting and restricting floods of unwanted messages is implemented by cluster analysis over time intervals. Application of streaming machine learning clustering algorithms enables finding clusters of messages (P2P text messages, WHATSAPP, tweets) sharing the same content. Such clusters may be analyzed for finding out offensive messages, unwanted or spam messages, and rumors and take corrective actions as needed. The solution enables visualization of data and/or messages and identification of clusters as the solution works on the data and aggregates data into clusters over time intervals. Corrective actions may be applied on selected clusters based on visualized data clusters or by automated application of defined rules.

Detection and restriction of unwanted messages through time interval cluster analysis

Application of streaming machine learning clustering algorithms enables finding clusters of messages (P2P text messages, WHATSAPP, tweets) sharing the same content. Such clusters may be analyzed for finding out offensive messages, unwanted or spam messages, and rumors and take corrective actions as needed. The solution enables visualization of data and/or messages and identification of clusters as the solution works on the data and aggregates data into clusters over time intervals. Corrective actions may be applied on selected clusters based on visualized data clusters or by automated application of defined rules.

Detection of flooding of unwanted messages

A system and method of detecting false Short Messaging Service (SMS) delivery reports utilizing machine learning. A statistical model is prepared based on attributes of SMS delivery reports that are known to be genuine. An anomaly detection algorithm compares attributes of the subsequent SMS messages against the statistical model. The anomaly detection algorithm is used to detect, and flag false SMS delivery reports returned by aggregators and operators.

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Karnataka, India

Ramakanth Vallur