Robert D Fish

US Patent:

20170093795, Mar 30, 2017

Filed:

Sep 24, 2015

Appl. No.:

14/864858

Inventors:

- Redmond WA, US
Viresh Ramdatmisier - Seattle WA, US
Barry Markey - Kirkland WA, US
Robert Fish - Seattle WA, US
Erik Tayler - Seattle WA, US
Dragos Boia - Seattle WA, US
Donald Ankney - Seattle WA, US

International Classification:

H04L 29/06

Abstract:

To protect network-based services, offering computer implemented functionality, from attacks, a passive web application firewall reactively identifies vulnerabilities, enabling such vulnerabilities to be quickly ameliorated, without intercepting communications or introducing other suboptimal aspects of traditional web application firewalls. Communications directed to the network-based services are logged and such logs are scanned for entries evidencing attacks, such as based on predetermined attack syntax. Further evaluation of the entries identified as evidencing attacks identifies a subset of those entries that correspond to likely successful attacks. Such further evaluation includes attacking the network-based service in an equivalent manner. Attacks that are found to be successful identify vulnerabilities, and a notification of such vulnerabilities is provided to facilitate amelioration of such vulnerabilities. Vulnerability amelioration can be automatic, such as by automatically adjusting the settings corresponding to the implementation of the network-based services to ameliorate identified vulnerabilities in a predetermined manner.

US Patent:

20160342500, Nov 24, 2016

Filed:

May 22, 2015

Appl. No.:

14/719834

Inventors:

- Redmond WA, US
Viresh Ramdatmisier - Seattle WA, US
Jiong Qiu - Sammamish WA, US
Barry Markey - Kirkland WA, US
Alisson A. S. Sol - Bellevue WA, US
Donald J. Ankney - Seattle WA, US
Eugene V. Bobukh - Kirkland WA, US
Robert D. Fish - Seattle WA, US

International Classification:

G06F 11/36
G06F 17/30

Abstract:

Template identification techniques for control of testing are described. In one or more implementations, a method is described to control testing of one or more services by one or more computing devices using inferred template identification. Templates are inferred, by the one or more computing devices, that are likely used for documents for respective services of a service provider that are available via corresponding universal resource locators (URLs) to form an inferred dataset. Overlaps are identified by the one or computing devices in the inferred dataset to cluster services together that have likely used corresponding templates. Testing is controlled by the one or more computing devices of the one or more services based at least in part on the clusters.

US Patent:

20160019387, Jan 21, 2016

Filed:

Oct 20, 2014

Appl. No.:

14/519062

Inventors:

- Redmond WA, US
Dragos D. Boia - Seattle WA, US
Barry Markey - Kirkland WA, US
Robert D. Fish - Seattle WA, US
Donald J. Ankney - Seattle WA, US
Viresh Ramdatmisier - Seattle WA, US

International Classification:

G06F 21/55

Abstract:

A behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. Machine learning hierarchical (agglomerative) clustering techniques are utilized to compute deviations between clustered data sets representing an “answer” that the service presents to a series of requests.

US Patent:

20160021124, Jan 21, 2016

Filed:

Jul 16, 2014

Appl. No.:

14/333377

Inventors:

- Redmond WA, US
Barry Markey - Kirkland WA, US
Robert D. Fish - Seattle WA, US
Donald J. Ankney - Seattle WA, US
Dragos D. Boia - Seattle WA, US
Viresh Ramdatmisier - Seattle WA, US

International Classification:

H04L 29/06

Abstract:

Various embodiments provide an approach to classifying security events based on the concept of behavior change detection or “volatility.” Behavior change detection is utilized, in place of a pre-defined patterns approach, to look at a system's behavior and detect any variances from what would otherwise be normal operating behavior. In operation, machine learning techniques are utilized as an event classification mechanism which facilitates implementation scalability. The machine learning techniques are iterative and continue to learn over time. Operational scalability issues are addressed by using the computed volatility of the events in a time series as input for a classifier. During a learning process (i.e., the machine learning process), the system identifies relevant features that are affected by security incidents. When in operation, the system evaluates those features in real-time and provides a probability that an incident is about to occur.

Address:

6522 4 Ave NE APT 27, Seattle, WA 98115

VIN:

1G6DW677670121036

Make:

CADILLAC

Model:

STS

Year:

2007