Microsoft Technology Licensing, LLC

Kumar S Srivastava

Geoffrey John Hulten

Eliot Carleton Gillum

Anatoliy Panasyuk

Ivan Osipkov

John Scarrow

One or more techniques and/or systems are disclosed for detecting and/or mitigating a potentially compromised online user account. One or more baselines can be established for a user's online account to determine a normal usage pattern for the account by the user (e.g., frequency of incoming/outgoing emails, text messages, etc.). The online user account can be periodically or continually monitored for use of the same resources used to determine the baseline(s). If a deviation from the baseline is detected, the deviation may be compared against a threshold to determine whether the deviation indicates that the account may be compromised. When an indication of a potentially compromised account is detected, the user can be notified of the indication, so that one or more actions can be taken to mitigate the potentially compromised account.

Detecting a compromised online user account

One or more techniques and/or systems are disclosed for mitigating machine solvable human interactive proofs (HIPs). A classifier is trained over a set of one or more training HIPs that have known characteristics for OCR solvability and HIP solving pattern from actual use. A HIP classification is determined for a HIP (such as from a HIP library used by a HIP generator) using the trained classifier. If the HIP is classified by the trained classifier as a merely human solvable classification, such that it may not be solved by a machine, the HIP can be identified for use in the HIP generation system. Otherwise, the HIP can be altered to (attempt to) be merely human solvable.

Mitigating use of machine solvable HIPs

Embodiments of message organization and spam filtering based on user interaction are presented herein. In an implementation, user interaction with a plurality of messages in a user interface is monitored, which includes analyzing an amount of time spent by a user in interacting with each message. Subsequent messages may then be filtered based on the monitored user interaction. In another implementation, messages are processed that are received via a network using a spam filter that was generated based on monitored interaction of a user with previous messages. The processing results in a value describing a relative likelihood of importance of each of the processed message to the user. The processed messages are then arranged for display in an order, one to another, in a user interface based on respective values.

Message organization and spam filtering based on user interaction

Functionality is described for addressing a threat to the security of a user device that utilizes a network-accessible service. The functionality operates by assessing the likelihood that the user device is infected by the undesirable item. When the user device makes a request to access the network-accessible service, the functionality can interact with the user device in a manner that is governed by the assessed likelihood that the user device is infected by the undesirable item.

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Sammamish, WA, United States of America

Kumar S Srivastava