Abstract: The invention proposes a method and system for deriving real-time insights about heterogeneous infrastructure by allowing a user to post a natural language query. A natural language processing (NLP) engine converts the natural language query into a computer identifiable query. A query engine (QE), based on the computer identifiable query, predicts diverse infrastructure-specific commands. The QE utilizes Machine Learning (ML) models to understand the intent of the natural language query and predict the diverse infrastructure-specific commands. The QE transforms and forwards the infrastructure-specific commands to corresponding components of the heterogeneous infrastructure. One or more sensors integrated with the components of the heterogeneous infrastructure receive query from the QE and respond to the queries in real-time. An interpreter module converts the responses received from the sensors into a common data format and derives insights from the converted responses and transmits them to the user device.

Abstract: An optical microstructure is configured to work with an optical fiber or a different substrate and the optical microstructure includes a beam converter including a tapered optical guide configured to transform a gaussian optical beam into a first annular optical beam; an inverted cone having first and second reflection surfaces, each configured to reflect the first annular optical beam, having a radius R1, so that a resulting second annular optical beam has a radius R2 larger than the radius R1; and a prism having a reflection surface configured to reflect the second annular optical beam to form a third converging annular optical beam. The third converging annular optical beam includes plural single optical beams that intersect at a given crossing point, outside the optical microstructure. The plural single optical beams form an optical trap.

Abstract: An optical microstructure is configured to work with an optical fiber or a different substrate and the optical microstructure includes a beam converter including a tapered optical guide configured to transform a gaussian optical beam into a first annular optical beam; an inverted cone having first and second reflection surfaces, each configured to reflect the first annular optical beam, having a radius R1, so that a resulting second annular optical beam has a radius R2 larger than the radius R1; and a prism having a reflection surface configured to reflect the second annular optical beam to form a third converging annular optical beam. The third converging annular optical beam includes plural single optical beams that intersect at a given crossing point, outside the optical microstructure. The plural single optical beams form an optical trap.

Abstract: Techniques for protecting against unauthorized technique support calls are disclosed. In one embodiment, the techniques may be realized as a system for protecting against unauthorized technique support calls including one or more computer processors. The one or more computer processors may be configured to register a client security application installed on a client device. The client security application may be associated with a mobile device. The client device may be separate from the mobile device. The one or more computer processors may further be configured to receive a notification to start monitoring the client device. The one or more computer processors may further be configured to monitor activities of the client device. The one or more computer processors may further be configured to alert a user of the client device for security risks associated with the activities.

Abstract: The disclosed computer-implemented method for detecting illegitimate voice calls may include (1) identifying an incoming voice call, (2) processing the incoming voice call in real time by (a) segmenting the incoming voice call into progressively produced call segments and, (b) for each new segment as the progressively produced call segments are produced, (A) extracting a set of features from the new segment and (B) feeding, as input into a neural network, the set of features and an output from the neural network generated based on a preceding segment of the incoming voice call, thereby generating a new output representing the current likelihood that the incoming voice call is illegitimate, (3) determining that the likelihood that the incoming voice call is illegitimate is above a predetermined threshold, and (4) performing a security action during the incoming voice call. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques for classifying and labeling data are disclosed. In one embodiment, the techniques may be realized as a system for classifying and labeling data comprising one or more processors. The one or more processors may be configured to distribute training data across a plurality of hosts. Each of the hosts may be assigned a random subset of the training data, and configured to cluster its own subset independently. The one or more processors may be further configured to label each cluster of the training data. The one or more processors may be further configured to receive new data, associate the new data with a plurality of the clusters of the training data, and assign the new data a label. The label may be chosen from labels of the plurality of the clusters. And the label may have a maximum associative factor of the new data.

Abstract: The disclosed computer-implemented method for automated generation of generic signatures used to detect polymorphic malware may include (1) clustering a set of polymorphic file samples that share a set of static attributes in common with one another, (2) computing a distance of the polymorphic file samples from a centroid that represents a reference data point with respect to the set of polymorphic file samples, (3) determining that the distance of the polymorphic file samples from the centroid is below a certain threshold, and then upon determining that the distance is below the certain threshold, (4) identifying, within the set of static attributes shared in common by the polymorphic file samples, a subset of static attributes whose values are identical across all of the polymorphic file samples and (5) generating a generic file-classification signature from the subset of static attributes. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method for automated generation of generic signatures used to detect polymorphic malware may include (1) clustering a set of polymorphic file samples that share a set of static attributes in common with one another, (2) computing a distance of the polymorphic file samples from a centroid that represents a reference data point with respect to the set of polymorphic file samples, (3) determining that the distance of the polymorphic file samples from the centroid is below a certain threshold, and then upon determining that the distance is below the certain threshold, (4) identifying, within the set of static attributes shared in common by the polymorphic file samples, a subset of static attributes whose values are identical across all of the polymorphic file samples and (5) generating a generic file-classification signature from the subset of static attributes. Various other methods, systems, and computer-readable media are also disclosed.