Rajeev Angal - Santa Clara CA Shivaram Bhat - Sunnyvale CA Michael Roytman - Glenview IL Subodh Bapat - Palo Alto CA
Assignee:
Sun Microsystems, Inc. - Santa Clara CA
International Classification:
H04L 1226
US Classification:
709224, 709218, 370352
Abstract:
Method and system for allowing a computer network operations manager to subscribe for and receive notifications concerning network events from one or more objects or object levels, as defined by distinguished name scoping, and optionally having at least one event characteristic from a selected list. The selected list of characteristics may include: one or more levels of network objects involved in the event; one or more specified network nodes involved in the event; a specified geographical region in which said event occurs; a specified period of days within which the event occurs or is initiated; a specified time interval within which the event occurs or is initiated; a specified class of devices involved in the event; and an event of one or more specified event types.
Alarm Manager System For Distributed Network Management System
MICHAEL ROYTMAN - GLENVIEW IL, US PLAMEN PETROV - BARRINGTON IL, US
International Classification:
G09G005/00
US Classification:
345/736000, 345/784000, 709/224000
Abstract:
The alarm manager display in a distributed network management system is arranged to have two modes of operation. In one mode of operation, the alarm manager display automatically scrolls when new events arrive. If there are sorting criteria defined, the alarm manager window scrolls either up or down depending on the sort order so that when new events arrive, they always appear on the screen. In the second mode of operation, the alarm manager window does not scroll when new events arrive. The scroll bar operational modes are selectable by an operator from a menu. In accordance with another embodiment, a special attribute is added to the alarm manager configuration file. This attribute is read when the alarm manager is started and places the alarm manager into the operational mode in which it was last used.
- Chicago IL, US Michael Roytman - Chicago IL, US Jeffrey Heuer - New York NY, US
International Classification:
G06F 21/57 G06N 20/00
Abstract:
Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.
- Chicago IL, US Michael Roytman - Chicago IL, US Jeffrey Heuer - New York NY, US
International Classification:
G06F 21/57 G06N 20/00
Abstract:
Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.
Vulnerability Assessment Based On Machine Inference
- Chicago IL, US Michael Roytman - Chicago IL, US David Bortz - Highland Park IL, US Jared Davis - Chicago IL, US
International Classification:
G06F 21/57 G06N 20/00 G06F 16/9032 G06F 16/903
Abstract:
Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.
- Chicago IL, US Michael Roytman - Chicago IL, US Jeffrey Heuer - New York NY, US
International Classification:
G06F 21/57 G06F 15/18
Abstract:
Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.
Facilitating Field Data Collection Using Hierarchical Surveys
The technology presented here enables low skilled administrators to design a hierarchical survey, low skilled field agents to collect answers to the hierarchical survey, and low skilled field managers to manage and monitor the progress of the field agents. The hierarchical surveys designed can be complex hierarchical surveys comprising multi-stage sampling units. The graphical user interfaces presented to the users are easy to use, and hide the complexity of the hierarchical survey. The user devices can communicate with each other to transmit the hierarchical surveys and the answers received to the hierarchical surveys using peer-to-peer networks, in environments where there is low, or no Internet connectivity.
Data Scientist at Risk I/O, Founder and COO at TruckSpotting, Inc.
Location:
Greater Chicago Area
Industry:
Information Services
Work:
Risk I/O - Chicago, IL since Dec 2012
Data Scientist
TruckSpotting, Inc. - Atlanta, GA since Sep 2011
Founder and COO
Enova Financial - Chicago, IL Sep 2012 - Dec 2012
Fraud Analytics Associate
Georgia Institute of Technology, Intel Corporation - Atlanta, GA Aug 2011 - Aug 2012
Research Assistant
Crowe Horwath LLP Jun 2010 - Aug 2011
Manufacturing Consultant
Education:
Georgia Institute of Technology 2011 - 2012
Master of Science (M.S.), Operations Research
University of Illinois at Urbana-Champaign 2007 - 2010
Bachelor of Science (B.S.), Industrial Engineering
Glenbrook North High School 2003 - 2007
Honor & Awards:
3rd Place, Chicago Startup Weekend Hackathon - WhereFi, 2012
President's Fellowship for Graduate Study, 2011-2012
Champion, National Coaches' Debate Association Tournament, 2007
Champion, Illinois State Debate Tournament, 2007
Blue Cross Blue Shield Association
Enterprise Architect
United Airlines Jan 2002 - Feb 2005
Senior Software Engineer
Novarra 2000 - 2001
Senior Software Engineer
Sun Microsystems Oct 1997 - Oct 2000
Senior Software Engineer
Motorola 1995 - 1997
Software Engineer
Education:
Lake Forest Graduate School of Management 2006 - 2009
Master of Business Administration, Masters, Management, Healthcare
Illinois Institute of Technology 1997 - 1999
Master of Science, Masters, Computer Science
University of Illinois at Chicago 1992 - 1993
Bachelors, Bachelor of Science, Mathematics, Computer Science
Tashkent State University 1988 - 1992
Bachelors, Bachelor of Science, Applied Mathematics
Skills:
Enterprise Architecture Sdlc Enterprise Software Soa It Strategy Agile Methodologies Integration Solution Architecture Business Analysis Software Project Management Scrum Requirements Analysis Software Development Sql Data Modeling Data Warehousing Business Intelligence Visio Vendor Management Db2 Agile Project Management Web Services Databases Web Applications Architecture Unix Database Design Pmo Requirements Gathering Microsoft Sql Server Systems Analysis Oracle Disaster Recovery Xml Java Enterprise Edition Sharepoint Application Architecture Soap It Management Software Design Java Software Engineering Tomcat Esb Software Documentation Mobile Technology Consumer Healthcare Social Media Ios Development Android Development
Interests:
Groupon Chicago Bears Yahoo Motorola Electro Music Personal Networking Finance Chicago Blackhawks Techno Music Manchester United Mashable Chicago Bulls Seinfeld (Tv Series) Applied Mathematics Premier League Computer Science United Airlines Chicago Directv