Microsoft Technology Licensing, LLC

Igal Figlin

Lior Arzi

Efim Hudis

Arthur Zavalkovsky

Khaja Ehteshamuddin Ahmed

Edward Wilkins Hardy

Jeffrey S Williams

Robert Eric Fitzgerald

Charles W Kaufman

Sterling M Reasor

Chengi Jimmy Kuo

Asad Yaqoob

Jennifer Lemond

Shailesh Padmakar Joshi

Bryan C Griffin

Vidyanand N Rajpathak

Eyal Zangi

Ahmad S Hussain

Jennifer R LeMond

Eric Radzikowski

A network security system employing multiple levels of processing to identify security threats. Multiple host machines may each contain an agent that detects possibilities of security threats based on raw data sensed locally at that host. The hosts may share information obtained from local analysis and each host may use information generated at one or more other hosts, in combination with information generated locally, to identify a security concern, indicating with greater certainty that a security threat exists. Based on security concerns generated by multiple hosts, a security threat may be to indicated and protective action may be taken.

Network intrusion detection with distributed correlation

The subject disclosure is directed towards protecting against malware, by classifying a user's risk level, which corresponds to a likelihood of malware being activated. To make the classification, data is collected that represents a probability of encountering malware, a probability of a user activating that malware, and the impact to the machine is activated. The classification maps to a protection level, which may be dynamically adjustable, e.g., based upon current risk conditions. The protection level determines a way to mitigate possible damage, such as by running a program as a virtualized program, running a virtualized operating system, or sandboxing a process.

Predictive malware threat mitigation

Providing a uniform security model to heterogeneous devices in a distributed computing environment. A method includes storing information about a plurality of device security models, including device credentials. The method further includes storing information about a uniform security model, including access rights for various principals. The method further includes identifying a principal. Based on the identified principal, access is provided to the principal according to the uniform security model, based on the device security model credentials.

Managing distributed operating system physical resources

Tracking malware state information assigned to computers in an enterprise network is described. A computer may transition from a current malware state to a new malware state in accordance with a plurality of stored rules and detection of an anti-malware event on the computer. Examples of anti-malware events include, but are not limited to, detection of new malware on the computer or cleaning of the computer. The malware state information for computers on the network may be mapped to a risk level representing an amount of risk that infected computers present to other computers on the network. The results of a risk level assessment for the computers on the network may be output via a user interface to enable an administrator of the network to prioritize servicing of computers with detected malware.

System and method for prioritizing computers based on anti-malware events

A network security system employing multiple levels of processing to identify security threats. Multiple host machines may each contain an agent that detects possibilities of security threats based on raw data sensed locally at that host. The hosts may share information obtained from local analysis and each host may use information generated at one or more other hosts, in combination with information generated locally, to identify a security concern, indicating with greater certainty that a security threat exists. Based on security concerns generated by multiple hosts, a security threat may be indicated and protective action may be taken.

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Igal Figlin