Microsoft Technology Licensing, LLC

Donald J Ankney

Viresh Ramdatmisier

Dragos Daniel Boia

Barry Markey

Robert Fish

Alisson A S Sol

Jiong Qiu

Eugene V Bobukh

Alisson Augusto Souza Sol

Karthik Selvaraj

Robert C Fruth

Peter Thayer

Jagannathan Deepak Manohar

Erik Tayler

Jason Matthew Conradt

Peter A Thayer

Embodiments provide for a security information and event management (SIEM) system utilizing distributed agents that can intelligently traverse a network to exfiltrate data in an efficient and secure manner. A plurality of agent devices can dynamically learn behavioral patterns and/or service capabilities of other agent devices in the networking environment, and select optimal routes for exfiltrating event data from within the network. The agent devices can independently, selectively, or collectively pre-process event data for purposes of detecting a suspect event from within the network. When a suspect event is detected, agent devices can select a target device based on the learned service capabilities and networking environment, and communicate the pre-processed event data to the target device. The pre-processed event data is thus traversed through the network along an optimal route until it is exfiltrated from the network and stored on a remote server device for storage and further analysis.

SIEM system and methods for exfiltrating event data

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.

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Seattle, WA, United States of America

Donald J Ankney