At&t Intellectual Property I, L.p.

At&t Technical Services Company, Inc.

Robert Archibald

Ganesh Subramaniam

Richard Hellstern

Srivathsan Srinivasagopalan

Creating and using learning models to identify botnet traffic can include obtaining netflow data associated with a connecting device that is communicating with a carrier network. The netflow data can represent communications associated with the connecting device. Data features associated with the communications can be extracted. The data features can include statistical information associated with the communications. A learning model based on the data features extracted from the netflow data can be trained. A prediction using the learning model can be generated, and an action based on the prediction can be taken.

Creating and using learning models to identify botnet traffic

Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

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Bridgewater, NJ, United States of America

Robert Archibald