Yahoo! Inc.

Microsoft Technology Licensing, LLC

Excalibur IP, LLC

Sundararajan Sellamanickam

Sathiya Keerthi Selvaraj

Vinod Sankaran Nair

Ram Shankar Siva Kumar

Marco DiPlacido

Nguyen Song Khanh Vu

Aniruddha Das

Matt Swann

Keerthi Selvaraj

Philip Lewis Bohannon

Arun Sundaresan Iyer

Cong Yu

Subhajit Sanyal

Mridul Muralidharan

Ashwin Machanavajjhala

Dhruv Kumar Mahajan

Priyanka Garg

A method and a system for summarizing a concept are provided. A query corresponding to a concept is received from a user. A plurality of images and corresponding descriptive information may be collected based on the query. The plurality of images and the descriptive information may be processed to form feature vectors and processed descriptive information respectively. Further, one or more topics may be identified for the plurality of images. Each of the plurality of images may be assigned with one or more topic distribution values corresponding to the one or more topics. The one or more topics correspond to the processed descriptive information. A sparse set of images may be determined based on the feature vectors and the assigned topic distribution values, to summarize the concept. Also, a target summary may be built from the summarized concept, by regularizing one or more distribution constraints.

Method and system for concept summarization

Lateral movement detection may be performed by employing different detection models to score logon sessions. The different detection models may be implemented by and/or utilize counts computed from historical security event data. The different detection models may include probabilistic intrusion detection models for detecting compromised behavior based on logon behavior, a sequence of security events observed during a logon session, inter-event time between security events observed during a logon session, and/or an attempt to logon using explicit credentials. Scores for each logon session that are output by the different detection models may be combined to generate a ranking score for each logon session. A list of ranked alerts may be generated based on the ranking score for each logon session to identify compromised authorized accounts and/or compromised machines. An attack graph may be automatically generated based on compromised account-machine pairs to visually display probable paths of an attacker.

Lateral movement detection

A system and method is described for large scale entity-specific classification of each entity-specific set of candidates in a collection of candidates for each specific entity in a collection of entities. The collection of entities may comprise a specific category or domain of entities (e.g. schools, restaurants, manufacturers, products, events, people). Candidates may comprise webpages or other resources with resource identifiers. Entity specific sets of candidates may be found by leveraging search engine query results and user interaction therewith for queries based on entity-specific attributes. The relationship(s) or class(es) for which candidate resources are being classified relative to a specific entity may comprise an authoritative, official home page (OHP), or other class (e.g. fan page, review, aggregator) relative to a specific entity. A feature generator generates entity-specific features for candidates. In accordance with its features, one or more classifiers rank each candidate for a specific class for a specific entity.

Large scale entity-specific resource classification

The present invention provides methods and systems for binary classification of items. Methods and systems are provided for constructing a machine learning-based and pairwise ranking method-based classification model for binary classification of items as positive or negative with regard to a single class, based on training using a training set of examples including positive examples and unlabelled examples. The model includes only one hyperparameter and only one threshold parameter, which are selected to optimize the model with regard to constraining positive items to be classified as positive while minimizing a number of unlabelled items classified as positive.

Pairwise ranking-based classifier

Generally, the present invention provides a method and computerized system for generating a classifier model, wherein the classifier model is operative to classify web content. The method and computerized system includes a first step of defining a plurality of predictive performance measures based on a leave one out (LOO) cross validation in terms of selectable model parameters. Exemplary predictive performance measures includes smoothened predictive measures such as F-measure, weighted error rate measure, area under curve measure, by way of example. The method and computerized system further includes deriving efficient analytical expressions for predictive performance measures to compute the LOO predictive performance and their derivatives. The next step is thereupon selecting a classifier model based on the LOO predictive performance.

System and method for generating a classifier model for classifying web content

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Bengaluru, India

Sundararajan Sellamanickam