Microsoft Technology Licensing, LLC

Viresh Ramdatmisier

Dragos Daniel Boia

Donald J Ankney

Barry Markey

Robert Fish

Aaron Ross Goldfeder

John M Hawkins

Alisson A S Sol

Jiong Qiu

Eugene V Bobukh

Alisson Augusto Souza Sol

Charles W Kaufman

Raja Krishnaswamy

Roberto Anthony Franco

Gregory Darrell Fee

Akhilesh Kaza

Tony Edward Schreiner

Mark A Alcazar

Cem Paya

Darren Mitchell

Jingyang Xu

Jeremiah S Epling

Pierre-Jean Reissman

Chris L Anderson

Stephen J Purpura

Andrew G Bybee

Karen Elizabeth Corby

Paul M Midgen

Jamie Lee Cool

Venkata Rama Prasad Tammana

Frank M Schwieterman

Joseph T Farro

Sergey A Khorun

Robert C Fruth

Jacqueline Lynette Reid

Andre A Needham

Jeff Cooperstein

Ariel Jorge Kirsman

Joseph J Gallagher

Erik Tayler

Systems and methods for automated selection of payloads for use in a security scan of a web application by a security scanner are described herein. More specifically, the systems and methods test potential payloads for a security scan of a given web application on a test application with known security vulnerabilities, evaluate valid response returned by this test application, determine functionally equivalent responses, group payloads based the equivalence of their valid responses, and select one or more payloads from each created group for use in the security scan of the given web application.

Security scanner

Various implementations provide an approach to control testing frequency based on behavior change detection. Behavior change detection is utilized, instead 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 at least some implementations, 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. In this way, the changes may be used to compute a volatility score, which the system uses to control testing frequency of one or more services, such as URLs that are part of a particular service.

Testing frequency control using a volatility score

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.

Passive web application firewall

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.

Template identification for control of testing

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.

Behavior change detection system for services

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.

Volatility-based classifier for security solutions

A method and system that provide an intuitive user interface and related components for making Internet users aware of Internet cookie-related privacy issues, and enabling users to control Internet privacy through automatic cookie handling. Default privacy settings for handling cookies are provided, and through the user interface, the privacy settings may be customized to a user's liking. Further, through the user interface, for each individual site that forms a page of content, the site's privacy policy may be reviewed and/or the privacy controlled by specifying how cookies from that site are to be handled. To make users aware, the user interface provides an active alert on a first instance of a retrieved web site's content that fails to include satisfactory privacy information, and thereafter, provides a distinctive passive alert to allow the user selective access to privacy information, per-site cookie handling and cookie handling settings.

Internet privacy user interface

Described is a mechanism for collectively evaluating security risks associated with loading an application. A hosting environment associated with loading the application invokes a trust manager to evaluate the security risks. The trust manager invokes a plurality of trust evaluators, where each trust evaluator is responsible for analyzing and assessing a different security risk. Upon completion of each security risk evaluation, results of those individual security risk evaluations are returned to the trust manager. The trust manager aggregates the variety of security risk evaluation results and makes a security determination based on the aggregated evaluation results. That determination may be to move forward with loading the application, to block the load of the application, or perhaps to prompt the user for a decision about whether to move forward with the load.

Mechanism for evaluating security risks

Described is a technology including an evaluation methodology by which a set of privileged code such as a platform's API method may be marked as being security critical and/or safe for being called by untrusted code. The set of code is evaluated to determine whether the code is security critical code, and if so, it is identified as security critical. Such code is further evaluated to determine whether the code is safe with respect to being called by untrusted code, and if so, is marked as safe. To determine whether the code is safe, a determination is made as to whether the first set of code leaks criticality, including by evaluating one or more code paths corresponding to one or more callers of the first set of code, and by evaluating one or more code paths corresponding to one or more callees of the first set of code.

Using security-related attributes

Testing of a software build. Differences between software builds are tracked by scanning the binaries of a software product to automatically discover its classes. A detailed dictionary is built that captures static and dynamic information of that build, including class dependencies. A comparison may be made with another build, so that selective tests may be automatically executed on any types, and their dependencies, that have had a structural or behavioral modification since the last build. Testers may load a set of constructors for any specific types to further increase coverage of types (or classes) tested. Detailed reports may also be provided that may be used to drive future testing work and target specific areas of the code for additional testing. Code generation from intermediate code to specified targets may also be performed to aid in reproducing and fixing bugs.

Method and system for automatically testing a software build

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Seattle, WA, United States of America

Viresh Ramdatmisier