Abstract: A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.

Abstract: A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.

Abstract: A bot detection service is enhanced by providing improved threat scoring using dynamic clustering of telemetry data received from a server. The detection approach leverages the notion that bot traffic is statistically anomalous compared to empirical models of human traffic. In particular, bots have more similar and repeating network patterns (i.e., lists of field values derived from client telemetry) that lend themselves to tighter clustering than random human traffic. The technique herein leverages this notion by providing a bot detection service with a network pattern clustering algorithm that differentiates bot-versus-human traffic by searching for similar and repeating network patterns that cluster together by virtue of having lesser variation (as compared to human traffic) in their field value sets.

Abstract: Methods and apparatus are described that enable consumers of media content to identify and navigate to content of interest. A graphical user interface (GUI) is provided in association with media content in which entities (e.g., keywords or distinct speakers) represented in the media content are presented in relation to the media timeline of the media content.

Abstract: Methods and apparatus are described that enable consumers of media content to identify and navigate to content of interest. A graphical user interface (GUI) is provided in association with media content in which entities (e.g., keywords or distinct speakers) represented in the media content are presented in relation to the media timeline of the media content.

Abstract: A bot detection service is enhanced by providing improved threat scoring using dynamic clustering of telemetry data received from a server. The detection approach leverages the notion that bot traffic is statistically anomalous compared to empirical models of human traffic. In particular, bots have more similar and repeating network patterns (i.e., lists of field values derived from client telemetry) that lend themselves to tighter clustering than random human traffic. The technique herein leverages this notion by providing a bot detection service with a network pattern clustering algorithm that differentiates bot-versus-human traffic by searching for similar and repeating network patterns that cluster together by virtue of having lesser variation (as compared to human traffic) in their field value sets.

Abstract: Methods and apparatus are described that enable consumers of media content to identify and navigate to content of interest. A graphical user interface (GUI) is provided in association with media content in which entities (e.g., keywords or distinct speakers) represented in the media content are presented in relation to the media timeline of the media content.

Abstract: Methods and apparatus are described that enable consumers of media content to identify and navigate to content of interest. A graphical user interface (GUI) is provided in association with media content in which entities (e.g., keywords or distinct speakers) represented in the media content are presented in relation to the media timeline of the media content.

Abstract: Disclosed are some implementations of methods, apparatuses, systems, and computer program products including non-transitory computer-readable storage media directed to anomaly detection. In some implementations, input vectors can be obtained in association with a learning process. The input vectors can be iteratively processed to compute a knowledge map. The input vectors can be iteratively processed to determine metadata associated with one or more knowledge elements. An anomaly value can be determined based on the knowledge map and the metadata. An alert can be raised indicating that an anomaly was detected if the anomaly value traverses a threshold.

Abstract: Methods and apparatus are described that enable consumers of media content to identify and navigate to content of interest. A graphical user interface (GUI) is provided in association with media content in which entities (e.g., keywords or distinct speakers) represented in the media content are presented in relation to the media timeline of the media content.

Abstract: Methods and apparatus are described that enable consumers of media content to identify and navigate to content of interest. A graphical user interface (GUI) is provided in association with media content in which entities (e.g., keywords or distinct speakers) represented in the media content are presented in relation to the media timeline of the media content.

Abstract: Methods, apparatuses and systems directed to pattern learning, recognition, and metrology. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In certain implementations, the present invention provides for methods and systems suitable for analyzing and recognizing patterns in biological signals such as multi-electrode array waveform data. In other implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. In other implementations, the present invention provides methods and systems for dynamic learning of patterns in supervised and unsupervised manners.

Abstract: Methods and apparatus are described that enable consumers of media content to identify and navigate to content of interest. A graphical user interface (GUI) is provided in association with media content in which entities (e.g., keywords or distinct speakers) represented in the media content are presented in relation to the media timeline of the media content.

Abstract: Methods, apparatuses and systems directed to pattern learning, recognition, and metrology. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In certain implementations, the present invention provides for methods and systems suitable for analyzing and recognizing patterns in biological signals such as multi-electrode array waveform data. In other implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. In other implementations, the present invention provides methods and systems for dynamic learning of patterns in supervised and unsupervised manners.

Abstract: Methods, apparatuses and systems directed to pattern learning, recognition, and metrology. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In certain implementations, the present invention provides for methods and systems suitable for analyzing and recognizing patterns in biological signals such as multi-electrode array waveform data. In other implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. In other implementations, the present invention provides methods and systems for dynamic learning of patterns in supervised and unsupervised manners.

Abstract: Methods, apparatuses and systems directed to pattern learning, recognition, and metrology. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In certain implementations, the present invention provides for methods and systems suitable for analyzing and recognizing patterns in biological signals such as multi-electrode array waveform data. In other implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. In other implementations, the present invention provides methods and systems for dynamic learning of patterns in supervised and unsupervised manners.

Abstract: Methods, apparatuses and systems directed to pattern learning, recognition, and metrology. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In certain implementations, the present invention provides for methods and systems suitable for analyzing and recognizing patterns in biological signals such as multi-electrode array waveform data. In other implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. In other implementations, the present invention provides methods and systems for dynamic learning of patterns in supervised and unsupervised manners.