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Haakon A Ringberg

from New York, NY
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Also known as:
Haakon Larsen
Connected to:
David R Ringberg, 40Mary J Ringbloom, 62Pontus C Ringblom, 58Daniel P Ringdahl, 58Gregory L Ringdahl, 35Katherine H Ringdahl, 44Marcus Ringdahl, 48Stephanie J Ringdahl, 58Arne Ringdal, 86Adam C Ringe, 41

Haakon Ringberg Phones & Addresses

  • New York, NY
  • Ossining, NY

Resumes

Resumes

Haakon Ringberg Photo 1

Software Engineering Manager

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Location:
121 west 19Th St, New York, NY 10011
Industry:
Computer Software
Work:
Google since Aug 2009
Senior Software Engineer
Education:
Princeton University 2004 - 2009
Cornell University 2001 - 2004
Haakon Ringberg Photo 2

Manager And Staff Software Engineer

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Location:
New York, NY
Industry:
Computer Software
Work:
Google
Manager and Staff Software Engineer
Haakon Ringberg Photo 3

Software Engineer

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Position:
Senior Software Engineer at Google
Location:
Greater New York City Area
Industry:
Computer Software
Work:
Google since Aug 2009
Senior Software Engineer
Education:
Princeton University 2004 - 2009
Cornell University 2001 - 2004

Publications

Us Patents

Systems And Methods For Rule-Based Anomaly Detection On Ip Network Flow

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US Patent:
20100153316, Jun 17, 2010
Filed:
Sep 28, 2009
Appl. No.:
12/568044
Inventors:
Nicholas Duffield - Summit NJ, US
Patrick Haffner - Atlantic Highland NJ, US
Balachander Krishnamurthy - New York NY, US
Haakon Andreas Ringberg - Ossining NY, US
Assignee:
AT&T Intellectual Property I, LP - Reno TX
International Classification:
G06F 21/00
G06F 15/18
US Classification:
706 12, 726 22, 706 47
Abstract:
A system to detect anomalies in internet protocol (IP) flows uses a set of machine-learning (ML) rules that can be applied in real time at the IP flow level. A communication network has a large number of routers that can be equipped with flow monitoring capability. A flow collector collects flow data from the routers throughout the communication network and provides them to a flow classifier. At the same time, a limited number of locations in the network monitor data packets and generate alerts based on packet data properties. The packet alerts and the flow data are provided to a machine learning system that detects correlations between the packet-based alerts and the flow data to thereby generate a series of flow-level alerts. These rules are provided to the flow time classifier. Over time, the new packet alerts and flow data are used to provide updated rules generated by the machine learning system.

Systems And Methods For Rule-Based Anomaly Detection On Ip Network Flow

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US Patent:
20160105462, Apr 14, 2016
Filed:
Dec 15, 2015
Appl. No.:
14/969591
Inventors:
- Atlanta GA, US
Patrick Haffner - Atlantic Highland NJ, US
Balachander Krishnamurthy - New York NY, US
Haakon Andreas Ringberg - Ossining NY, US
Assignee:
AT&T Intellectual Property I, L.P. - Atlanta GA
International Classification:
H04L 29/06
G06N 5/02
H04L 12/721
H04L 12/26
H04L 12/24
Abstract:
A system to detect anomalies in internet protocol (IP) flows uses a set of machine-learning (ML) rules that can be applied in real time at the IP flow level. A communication network has a large number of routers equipped with flow monitoring capability. A flow collector collects flow data from the routers throughout the communication network and provides them to a flow classifier. At the same time, a limited number of locations in the network monitor data packets and generate alerts based on packet data properties. The packet alerts and the flow data are provided to a machine learning system that detects correlations between the packet-based alerts and the flow data to thereby generate a series of flow-level alerts. These rules are provided to the flow time classifier. Over time, the new packet alerts and flow data are used to provide updated rules generated by the machine learning system.
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Haakon A Ringberg from New York, NY Get Report