Cylance, Inc.

United States of America

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IPC Class
G06F 21/50 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems 1
G06F 9/06 - Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs 1
G06F 9/44 - Arrangements for executing specific programs 1
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU] 1
Found results for  patents

1.

APPLICATION EXECUTION CONTROL UTILIZING ENSEMBLE MACHINE LEARNING FOR DISCERNMENT

      
Document Number 02938580
Status In Force
Filing Date 2015-02-06
Open to Public Date 2015-08-13
Grant Date 2022-08-16
Owner CYLANCE INC. (USA)
Inventor
  • Permeh, Ryan
  • Soeder, Derek A.
  • Chisholm, Glenn
  • Russell, Braden
  • Golomb, Gary
  • Wolff, Matthew
  • Mcclure, Stuart

Abstract

Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

IPC Classes  ?

  • G06F 9/06 - Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs

2.

STATIC FEATURE EXTRACTION FROM STRUCTURED FILES

      
Document Number 02938266
Status In Force
Filing Date 2015-01-30
Open to Public Date 2015-08-06
Grant Date 2024-01-02
Owner CYLANCE INC. (USA)
Inventor
  • Soeder, Derek A.
  • Permeh, Ryan
  • Golomb, Gary
  • Wolff, Matthew

Abstract

Data is received or accessed that includes a structured file encapsulating data required by an execution environment to manage executable code wrapped within the structured file. Thereafter, code and data regions are iteratively identified in the structured file. Such identification is analyzed so that at least one feature can be extracted from the structured file. Related apparatus, systems, techniques and articles are also described.

IPC Classes  ?

  • G06F 21/50 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems

3.

GENERATION OF API CALL GRAPHS FROM STATIC DISASSEMBLY

      
Document Number 02938265
Status In Force
Filing Date 2015-01-30
Open to Public Date 2015-08-06
Grant Date 2024-02-06
Owner CYLANCE INC. (USA)
Inventor
  • Soeder, Derek A.
  • Wolff, Matthew

Abstract

Data is received that includes at least a portion of a program. Thereafter, entry point locations and execution-relevant metadata of the program are identified and retrieved. Regions of code within the program are then identified using static disassembly and based on the identified entry point locations and metadata. In addition, entry points are determined for each of a plurality of functions. Thereafter, a set of possible call sequences are generated for each function based on the identified regions of code and the determined entry points for each of the plurality of functions. Related apparatus, systems, techniques and articles are also described.

IPC Classes  ?

  • G06F 9/44 - Arrangements for executing specific programs

4.

AUTOMATED SYSTEM FOR GENERATIVE MULTIMODEL MULTICLASS CLASSIFICATION AND SIMILARITY ANALYSIS USING MACHINE LEARNING

      
Document Number 02915383
Status In Force
Filing Date 2014-06-24
Open to Public Date 2014-12-31
Grant Date 2023-05-02
Owner CYLANCE INC. (USA)
Inventor
  • Permeh, Ryan
  • Mcclure, Stuart
  • Wolff, Matthew
  • Golomb, Gary
  • Soeder, Derek A.
  • Levites, Seagen
  • O'Dea, Michael
  • Acevedo, Gabriel
  • Chisholm, Glenn

Abstract

A sample of data is placed within a directed graph that comprises a plurality of hierarchical nodes that form a queue of work items for a particular worker class that are used to process the sample of data. Subsequently, work items are scheduled within the queue for each of a plurality of workers by traversing the nodes of the directed graph. The work items are then served to the workers according to the queue. Results can later be received from the workers for the work items (the nodes of the directed graph are traversed based on the received results). In addition, in some variations, the results can be classified so that one or models can be generated. Related systems, methods, and computer program products are also described.

IPC Classes  ?

  • G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]