Abstract: An approach for establishing a priority ranking for endpoints in a network. This can be useful when triaging endpoints after an endpoint becomes compromised. Ensuring that the most critical and vulnerable endpoints are triaged first can help maintain network stability and mitigate damage to endpoints in the network after an endpoint is compromised. The present technology involves determining a criticality ranking and a secondary value for a first endpoint in a datacenter. The criticality ranking and secondary value can be combined to form priority ranking for the first endpoint which can then be compared to a priority ranking for a second endpoint to determine if the first endpoint or the second endpoint should be triaged first.

Abstract: An example method can include receiving a traffic report from a sensor and using the traffic report to detect intra-datacenter flows. These intra-datacenter flows can then be compared with a description of historical flows. The description of historical flows can identify characteristics of normal and malicious flows. Based on the comparison, the flows can be classified and tagged as normal, malicious, or anomalous. If the flows are tagged as malicious or anomalous, corrective action can be taken with respect to the flows. A description of the flows can then be added to the description of historical flows.

Abstract: Systems, methods, and computer-readable media for determining sensor placement and topology. In some embodiments, a system can receive messages from sensors deployed around a network, each of the messages reporting a respective flow captured by a reporting sensor from the sensors. Next, the system can identify flows reported in the messages and, for each of the flows, generate a respective list of sensors that reported that flow. Based on the respective list of sensors, the system can infer a respective placement of the sensors within the network and a topology of the sensors. For example, the system can determine that a first sensor is deployed in a virtual machine, a second sensor is deployed in a hypervisor hosting the virtual machine, and a third sensor is deployed in a network device configured to route traffic associated with the hypervisor.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: A system and method for recommending content to a user in a social network, including: logging user activity for the user in the social network; categorizing the user activity across all the user's networks, wherein each category is assigned a score based on relevance to the user; assigning weights to the user activities; calculating a social index score as a function of the weighted user activity categories; logging user content into categories; scoring the user content; and generating a content social index by weighting the content scores.

Abstract: Described herein are solutions for determining quality of online ads and matching the ads to content so that the content is not devalued by the ads. Such solutions may also identify relationships between ads and their influence on user engagement with host content. The solutions may also define and provide the relationships to advertisers, in forms of historical scores and projected scores. The historical scores may include historical elasticity scores and the projected scores may include projected elasticity scores. The scores may be determined per ad and content pair. The solutions can use the scores to influence ad pricing.

Abstract: In one embodiment, a group of two or more individuals may be identified. A group profile associated with the group of two or more individuals may be obtained. One or more content items and/or one or more advertisements suitable for presenting to the group of two or more individuals may be identified based, at least in part, upon the group profile. The one or more content items and/or one or more advertisements may be provided for access by the group of two or more individuals.

Abstract: Techniques for providing offers for products/services associated with a topic to users based on a measure of conversion determined for the users are described. The measure of conversion for each user may be indicative of a probability that the user will purchase item(s) associated with a particular topic. The measure of conversion may be based on social activity associated with the user and social activity associated with other users that have formed a social connection with the user. Offers associated with the particular topic are presented to the user in response to determining that the measure of conversion of the user for the topic reaches a predetermined threshold and/or matches further criteria.

Abstract: A system and method for recommending content to a user in a social network, including: logging user activity for the user in the social network; categorizing the user activity across all the user's networks, wherein each category is assigned a score based on relevance to the user; assigning weights to the user activities; calculating a social index score as a function of the weighted user activity categories; logging user content into categories; scoring the user content; and generating a content social index by weighting the content scores.

Abstract: A system and method for monetizing user-generated web content are provided. The system includes a tracking engine operable to track user activity taking place a plurality of social networks. The tracking engine is in communication with an branding engine, which is notified when a user publishes content on a social network. The ad branding engine is configured to analyze user-published content and select at least one advertisement contextually related thereto. The ad branding engine incorporates the advertisement with the user-published content, and in turn, the user is compensated.

Abstract: Embodiments are directed towards automatically organizing an event for users to participate in an activity. The system can display suggestions for a plurality of activities to a user. Further, the system may selectively display information about at least one other user with at least one activity of the plurality of activities. The at least one other user may be identified based on the at least one other user having an interest in the at least one activity. Thus, the user may be able to view other users who are interested in an activity and may be nudged to also be interested in the activity. When a minimum number of other users indicate an explicit interest for an activity, the system can automatically organize an event for the users for the activity. The event may be a physical offline gathering of users that are interested in the activity.

Abstract: Particular embodiments of the present invention are directed to systems and methods for predicting the outcome of events based on internet activity associated with such events. For example, an internet activity metric associated with one or more events may be determined. The internet activity metric may be based at least on the popularity of particular search terms related to the events. The outcomes of the events may also be determined. The determined internet activity metrics and the outcomes of the one or more events may be analyzed to generate an algorithm for predicting the outcomes of subsequent events. For example, an internet activity metric associated with a particular subsequent event (e.g., based at least on the popularity of a particular search term related to the subsequent event) may be determined, and the algorithm may be applied to the internet activity metric to predict the outcome of the particular subsequent event.

Abstract: A system and method for monetizing user-generated web content are provided. The system includes a tracking engine operable to track user activity taking place a plurality of social networks. The tracking engine is in communication with an branding engine, which is notified when a user publishes content on a social network. The ad branding engine is configured to analyze user-published content and select at least one advertisement contextually related thereto. The ad branding engine incorporates the advertisement with the user-published content, and in turn, the user is compensated.

Abstract: A system and method for dynamically monetizing keyword values based on a correlation between one or more indices. The method includes generating a first index for a subject being searched by users via web server, and a second index for the subject based on a corresponding level of popularity. The first index is correlated with the second index to predict a future second index for the subject based on the first index.

Abstract: Embodiments are directed towards determining dependent interest affinity values between users to identify users that may mirror interests and thereby have an increased probability of becoming friends. A plurality of tracked online activities are classified into a plurality of interests categories, and used to determine weighted scores for each interest based on a quantity and quality of related activities for the interest. A proportional score for each interest is also determined and used with the weighed scores to generate dependent interest affinities between pairs of users. Interest indices are obtained and rank ordered for a given user and another user based on relevant dependent interest affinities. The resulting interest indices may be filtered based on a variety of criteria. At least some information about the related other users may be displayed to the given user based on the rank ordering, as possible mirrored friends.

Abstract: A system and method for dynamically monetizing keyword values based on a correlation between one or more indices. The method includes generating a first index for a subject being searched by users via web server, and a second index for the subject based on a corresponding level of popularity. The first index is correlated with the second index to predict a future second index for the subject based on the first index.