At&t Intellectual Property I, L.p.

Fei Wu

Carolyn Roche Johnson

Swapna Buccapatnam Tirumala

Arun Jotshi

Yaron Kanza

Brian Dean Freeman

Kathleen S Meier-Hellstern

Danielle Liu

Tuan Duong

Velin Kounev

Gaurav Thakur

Steven A Monetti

Kartik Pandit

Richard Koch

Mark Lyn

Divith Aruni Babu

A processing system may obtain a first sampled flow record for a first flow in a network, comprising information regarding selected packets of the first flow, derive, from the first sampled flow record, a data volume and a duration of the first flow, and determine a first flow metric for the first flow that is calculated from the data volume and the duration, where the first flow metric is one of a plurality of flow metrics for a plurality of flows, and where the plurality of flow metrics is determined from the plurality of sampled flow records associated with the plurality of flows. The processing system may then classify the first flow into one of at least two classes, based upon the first flow metric and at least a first flow metric threshold.

Traffic classification of elephant and mice data flows in managing data networks

Domains and IPs are scored using domain resolution data to identify malicious domains and IPs. A domain and IP resolution graph for a set of domains and IPs in a system. A seed set of known malicious domains and known malicious IPs is selected from a malicious domain and malicious IP database. A graphical probabilistic propagation inference from the domain and IP resolution graph and the seed set of known malicious domains and known malicious IPs is generated. A malicious score is calculated for each domain in the set of domains and each IP in the set of IPs, and the malicious domain and malicious IP database is updated.

Scoring domains and IPS using domain resolution data to identify malicious domains and IPS

Concepts and technologies are disclosed herein for creating and using network coverage models. A request for a predicted coverage model that represents a first signal propagation in a first portion of a network that covers a first area associated with a first geographic location can be received. An aerial image that depicts the first area can be obtained. The aerial image can be provided to an existing coverage model. The existing coverage model can include a neural network, and the existing coverage model can be based on a second signal propagation in a second portion of the network that covers a second area associated with a second location. The predicted coverage model for the first area can be obtained from the existing coverage model.

Creating and using network coverage models

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Jersey City, NJ, United States of America

Fei Wu