CPC H04L 43/045 (2013.01) [G06F 3/0482 (2013.01); G06F 3/04842 (2013.01); G06F 3/04847 (2013.01); G06F 9/45558 (2013.01); G06F 16/122 (2019.01); G06F 16/137 (2019.01); G06F 16/162 (2019.01); G06F 16/17 (2019.01); G06F 16/173 (2019.01); G06F 16/174 (2019.01); G06F 16/1744 (2019.01); G06F 16/1748 (2019.01); G06F 16/235 (2019.01); G06F 16/2322 (2019.01); G06F 16/2365 (2019.01); G06F 16/248 (2019.01); G06F 16/24578 (2019.01); G06F 16/285 (2019.01); G06F 16/288 (2019.01); G06F 16/29 (2019.01); G06F 16/9535 (2019.01); G06F 21/53 (2013.01); G06F 21/552 (2013.01); G06F 21/556 (2013.01); G06F 21/566 (2013.01); G06N 20/00 (2019.01); G06N 99/00 (2013.01); G06T 11/206 (2013.01); H04J 3/0661 (2013.01); H04J 3/14 (2013.01); H04L 1/242 (2013.01); H04L 9/0866 (2013.01); H04L 9/3239 (2013.01); H04L 9/3242 (2013.01); H04L 41/046 (2013.01); H04L 41/0668 (2013.01); H04L 41/0803 (2013.01); H04L 41/0806 (2013.01); H04L 41/0816 (2013.01); H04L 41/0893 (2013.01); H04L 41/12 (2013.01); H04L 41/16 (2013.01); H04L 41/22 (2013.01); H04L 43/02 (2013.01); H04L 43/026 (2013.01); H04L 43/04 (2013.01); H04L 43/062 (2013.01); H04L 43/08 (2013.01); H04L 43/0805 (2013.01); H04L 43/0811 (2013.01); H04L 43/0829 (2013.01); H04L 43/0841 (2013.01); H04L 43/0858 (2013.01); H04L 43/0864 (2013.01); H04L 43/0876 (2013.01); H04L 43/0882 (2013.01); H04L 43/0888 (2013.01); H04L 43/10 (2013.01); H04L 43/106 (2013.01); H04L 43/12 (2013.01); H04L 43/16 (2013.01); H04L 45/306 (2013.01); H04L 45/38 (2013.01); H04L 45/46 (2013.01); H04L 45/507 (2013.01); H04L 45/66 (2013.01); H04L 45/74 (2013.01); H04L 47/11 (2013.01); H04L 47/20 (2013.01); H04L 47/2441 (2013.01); H04L 47/2483 (2013.01); H04L 47/28 (2013.01); H04L 47/31 (2013.01); H04L 47/32 (2013.01); H04L 61/5007 (2022.05); H04L 63/0227 (2013.01); H04L 63/0263 (2013.01); H04L 63/06 (2013.01); H04L 63/0876 (2013.01); H04L 63/145 (2013.01); H04L 63/1408 (2013.01); H04L 63/1416 (2013.01); H04L 63/1425 (2013.01); H04L 63/1433 (2013.01); H04L 63/1441 (2013.01); H04L 63/1458 (2013.01); H04L 63/1466 (2013.01); H04L 63/16 (2013.01); H04L 63/20 (2013.01); H04L 67/01 (2022.05); H04L 67/10 (2013.01); H04L 67/1001 (2022.05); H04L 67/12 (2013.01); H04L 67/51 (2022.05); H04L 67/75 (2022.05); H04L 69/16 (2013.01); H04L 69/22 (2013.01); H04W 72/54 (2023.01); H04W 84/18 (2013.01); G06F 2009/4557 (2013.01); G06F 2009/45587 (2013.01); G06F 2009/45591 (2013.01); G06F 2009/45595 (2013.01); G06F 2221/033 (2013.01); G06F 2221/2101 (2013.01); G06F 2221/2105 (2013.01); G06F 2221/2111 (2013.01); G06F 2221/2115 (2013.01); G06F 2221/2145 (2013.01); H04L 67/535 (2022.05)] | 20 Claims |
1. A network traffic monitoring system comprising:
a plurality of distributed sensors, each sensor associated with a particular device of a plurality of physical or virtual devices, wherein:
each sensor generates network flow data based upon packets sent and/or received via a network interface local to the particular device associated with that sensor;
a first device of the plurality of physical or virtual devices is associated with at least one first sensor of the plurality of distributed sensors and comprises a virtual machine;
a second device of the plurality of physical or virtual devices is associated with at least one second sensor and comprises a container; and
a third device of the plurality of physical or virtual devices is associated with at least one third sensor comprises a network switch; and
a backend comprising a collector, an analytics module, and a presentation module, wherein the collector includes a storage, and wherein the presentation module includes one or more application programming interface (API) segments;
wherein the collector is communicably attached to a communications network and receives a plurality of network flow data from the plurality of distributed sensors via the attached communications network,
wherein the analytics module evaluates the plurality of network flow data to establish patterns of a particular behavior of the plurality of physical or virtual devices, and uses a machine learning model to evaluate received information from the plurality of network flow data, and
wherein upon identifying received information that varies from the machine learning model of the particular behavior of the plurality of physical or virtual devices, the system provides, via the presentation module, a report of anomalous flow data.
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