Microsoft Technology Licensing, LLC

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Amar Dinesh Patel

Charles Neil Boyd

Michael A Toelle

Stephen Harry Wright

Iouri Tarassov

Craig C Peeper

Michele Bethany Boland

Anantha Rao Kancherla

Blake Douglas Pelton

Peter-Pike Johannes Sloan

David R Blythe

Jeff M J Noyle

Samuel Z Glassenberg

Matthew David Sandy

Max Alan McMullen

Yuri Dotsenko

Austin Neil Kinross

Jesse Natalie

Steve Pronovost

Ravi Chandru Shahani

Shawn L Hargreaves

Douglas John Hines

Thomas Lee Davidson

Chas Boyd

Leonardo Esteban Blanco

Mario C Goertzel

Anuj Bharat Gosalia

Kristjan Eric Hatlelid

David Floyd Aronson

Miles Cohen

Nitin Kumar Goel

Daniel Kurt Baker

John Robert Lambert

Allison Klein

Loren McQuade

Abhijeet Surendra Hatekar

Shai Hinitz

Anantha R Kancheria

Juilee Rege

Meng Yang

Michael McCanney

David P Jaeger

Revanth Rameshkumar

Ethan Jacob Holland

Matt Lee

Mario Davis Goertzel

William Kristiansen

Kristjan E Hatlelid

IPRID reputation assessment enhances cybersecurity. IPRIDs include IP addresses, domain names, and other network resource identities. A convolutional neural network or other machine learning model is trained with data including aggregate features or rollup features or both. Aggregate features may include aggregated submission counts, classification counts, HTTP code counts, detonation statistics, and redirect counts, for instance. Rollup features reflect hierarchical rollups of data using &#x3c;unknown&#x3e; value placeholders specified in IPRID templates. The trained model can predictively infer a label, or produce a rapid lookup table of IPRIDs and maliciousness probabilities. Training data may be organized in grids with rows, columns, planes, branches, and slots. Training data may include whois data, geolocation data, and tenant data. Training data tuple sets may be expanded by date or by original IPRID. Trained models can predict domain labels accurately at scale, even when most of the domains encountered have never been classified before.

Predictive internet resource reputation assessment

Potentially malicious uniform resource locators and websites are safely and effectively investigated through live forensic browsing. Live data from an isolated browser feeds a security information and event management (SIEM) tool and other forensic tools during a browsing session, allowing investigators to direct the browsing in response to analysis results. Session data may be translated for SIEM ingestion. Browsing sessions may be manually or automatically customized to obscure their forensic nature, by routing selection, by bandwidth or latency adjustment, or by spoofing externally detectable characteristics such as geolocation, user agent, time zone, and language. Forensic activity by an investigator may also be obscured from discovery by an attacker as a result of spoofing the browser's context, such as plugin status and host machine physical characteristics. Human presence tests relied on by attackers may be satisfied without sacrificing a targeted system's cybersecurity or an investigator's access to forensic tools.

Live forensic browsing of URLs

Cybersecurity enhancements help avoid malicious Uniform Resource Locators (URLs). Embodiments may reduce or eliminate reliance on subjective analysis or detonation virtual machines. URL substrings are automatically analyzed for maliciousness using malice patterns. Patterns may test counts, lengths, rarity, encodings, and other inherent aspects of URLs. URLs may be analyzed individually, or in groups to detect shared portions, or both. URL analysis may use or avoid machine learning, and may use or avoid lookups. Malice patterns may be used individually or in combinations to detect malicious URLs. Analysis results may enhance security through blocking use of suspect URLs, flagging them for further analysis, or allowing their validated use, for instance. Analysis results may also be fed back to further train a machine learning model or a statistical model.

Uniform resource locator security analysis using malice patterns

Examples described herein generally relate to generating a visualization of an image. A proprietary structure that specifies ray tracing instructions for generating the image using ray tracing is intercepted from a graphics processing unit (GPU) or a graphics driver. The proprietary structure can be converted, based on assistance information, to a visualization structure for generating the visualization of the image. The visualization of the image can be generated from the visualization structure.

Techniques for generating visualizations of ray tracing images

Examples described herein generally relate to intercepting, from a graphics processing unit (GPU) or a graphics driver, a buffer that specifies one or more shader records of a shader table to use in generating the image using raytracing, determining, based at least in part on an identifier of the one or more shader records, a layout of the one or more shader records, interpreting, based at least in part on the layout, additional data in the buffer to determine one or more parameters corresponding to the one or more shader records, and displaying, via an application, an indication of the one or more parameters on an interface.

Techniques for displaying a shader table associated with raytracing images

Providing safe access of a data item accessed through one of a plurality of access channels while concurrently providing a policy check of the data item. An indication associated with accessing a data item through one access channel of a plurality of access channels may be received. In response to receiving the indication associated with accessing the data item, the data item may be automatically analyzed to determine whether the data item satisfies a policy. Also in response to receiving the indication associated with accessing the data item and while determining whether the data item satisfies the policy, safe access of the data item may be provided. Regardless of the access channel through which the data item was accessed, any of the policy check, the safe access, and the analysis of the data item may be the same.

Safe data access through any data channel

The application programming interface permits an application to specify resources to be used by shaders, executed by the GPU, through a data structure called the 'root arguments.' A root signature is a data structure in an application that defines the layout of the root arguments used by an application. The root arguments are a data structure resulting from the application populating locations in memory according to the root signature. The root arguments can include one or more constant values or other state information, and/or one or more pointers to memory locations which can contain descriptors, and/or one or more descriptor tables. Thus, the root arguments can support multiple levels of indirection through which a GPU can identify resources that are available for shaders to access.

Configuring resources used by a graphics processing unit

Methods and computer-storage media are provided for rendering three-dimensional (3D) graphics by tessellating objects using novel structures and algorithms. Rendering utilizing 'patches,' configurable functions that include a specified number of control points, allows for computation on a per-patch or per-control-point basis, in addition to traditional per-vertex, per-primitive, and per-pixel methods. This produces a number of advantages over previous tessellation methods, including the reuse of computations across existing vertices and the ability to process at a lower frequency. The operations to compute points are simplified in order to optimize system resources used in the process. Transitions from un-tessellated to tessellated objects are smoother utilizing the present invention, while developers have more flexibility in the level of detail present at different edges of the same patch. Detail within a displacement map also can be increased without negative effects associated with previous systems and methods.

Efficient geometric tessellation and displacement

In one embodiment, a graphics processing unitmay support a logical resource using a physical tile poolfor sparse data sets. The graphics processing unitmay allocate a physical memory allocation into a primary physical tile pool. The graphics processing unitmay define a mapping for a logical tile setfor a logical resource. The graphics processing unitmay selectively map a primary logical tileof the logical tile setto a primary physical tileof the primary physical tile pool

Hardware-accelerated resource tiling

A procedural texture relates texel coordinates to color values through an arbitrary function, herein called a texel shader. The procedural texture is defined by a dimension, size, texel format and the texel shader. Texel coordinates are an input to the texel shader, which generates a color value for those texel coordinates. A renderer can be implemented either in hardware, such as part of a graphics processor, or in software as a computer program executed by a processor. The renderer samples from the procedural texture in response to texel coordinates, and evaluates the texel shader on demand. Filtering also can be applied automatically to results. The results of the texel shader invocations are stored in a texture cache to take advantage of spatial and temporal locality. Results are shared among threads, processes and the like through the texture cache.

Procedurally defined texture maps

In one example, a graphic computing device may apply a clipping technique to accurately and efficiently render a graphic data set. A central processing unit may generate a convex polygonal clip from a two-dimensional polygon. The central processing unit may calculate a clipping plane for a convex polygonal clip based on an edge of the convex polygonal clip. A graphics processor may apply the convex polygonal clip in a pixel shader.

Convex polygon clipping during rendering

A resource used by a shader executed by a graphics processing unit is referenced using a 'descriptor'. Descriptors are grouped together in memory called a descriptor heap. Applications allocate and store descriptors in descriptor heaps. Applications also create one or more descriptor tables specifying a subrange of a descriptor heap. To bind resources to a shader, descriptors are first loaded into a descriptor heap. When the resources are to be used by a set of executing shaders, descriptor tables are defined on the GPU identifying ranges within the descriptor heap. Shaders, when executing, refer to the currently defined descriptor tables to access the resources made available to them. If the shader is to be executed again with different resources, and if those resources are already in memory and specified in the descriptor heap, then the descriptor tables are changed to specify different ranges of the descriptor heap.

A method for tile-based rendering of content. Content may be rendered in a memory region organized as multiple tiles. In scenarios in which content is generated in layers, for operations that involve compositing image layers, an order in which portions of the image are processed may be selected to reduce the aggregate number of memory accesses times, which in turn may improve the performance of a computer that uses tile-based rendering. An image may be processed such that operations relating to rendering portions of different layers corresponding to the same tile are performed sequentially. Such processing may be used in a computer with a graphics processing unit that supports tile-based rendering, and may be particularly well suited for computers with a slate form factor. An interface to a graphics processing utility within the computer may provide a flag to allow an application to specify whether operations may be reordered.

System and method for layering using tile-based renderers

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Issaquah, WA, United States of America

Amar Dinesh Patel