Emc IP Holding Company LLC

Dell Products Inc.

Or Herman Saffar

Amihai Savir

Yevgeni Gehtman

Oron Golan

Noga Gershon

Roie Ben Eliyahu

Maxim Balin

Tomer Shachar

Anat Parush Tzur

Ofir Ezrielev

Avitan Gefen

Omer Sagi

Oshry Ben-Harush

Raul Shnier

Aviram Fireberger

John Lawrence Dalton

Alon Shitrit

Mridul Vinay Garg

Techniques are provided for firmware protection using multi-chip storage of firmware images. One method comprises obtaining a firmware image; encrypting the firmware image; splitting the encrypted firmware image into a plurality of encrypted firmware image portions; and storing the plurality of encrypted firmware image portions on a plurality of recovery chips, wherein a threshold number of the encrypted firmware image portions from at least two different recovery chips are needed to reconstruct the firmware image. The threshold number of the encrypted firmware image portions can be obtained from the at least two different recovery chips and a validation can be applied to the obtained encrypted firmware image portions. The threshold number of encrypted firmware image portions may be obtained in response to a chip that stores the firmware image being inactive.

Firmware protection using multi-chip storage of firmware image

Techniques are provided for software code refactoring prioritization using software code defect aggregation in a graphical code representation. One method comprises obtaining an indication of an error in software code; obtaining a graphical representation of the software code, wherein each node in the graphical representation corresponds to a function and wherein nodes are connected based on a hierarchical relationship; assigning a weight to a first node that corresponds to a function associated with the error; assigning a decayed weight to an additional node, selected based on the hierarchical relationship with the first node, wherein the decayed weight is determined by applying a decay function to the weight assigned to the first node; prioritizing nodes in the graphical representation for a code refactoring of the software code based on the weights assigned to each node; and initiating an automated action based on a result of the prioritizing.

Software code refactoring prioritization using software code defect aggregation in graphical code representation

Techniques are provided for sentiment-based adaptations of user representations in virtual environments. One method comprises obtaining information characterizing a first user and a second user interacting with a session of a virtual environment; applying the information to an analytics engine to obtain a sentiment status indicating a sentiment of the second user in the virtual environment, wherein the analytics engine processes the obtained sentiment status of the second user to select an adaptation of a facial expression and/or a body language of a representation of the first user from learned sentiment-based facial expression examples and/or learned sentiment-based body language examples, using the obtained sentiment status of the second user; and automatically initiating a rendering of the virtual environment using the selected adaptation of the facial expression and/or the body language of the representation of the first user in the virtual environment.

Sentiment-based adaptations of user representations in virtual environments

Techniques are provided for estimating a delivery time for a product in a supply chain. One method comprises obtaining an order for at least one product; calculating a similarity-based feature and/or a proximity-based feature for the order; and applying the calculated similarity-based feature and/or the calculated proximity-based feature for the order to a machine learning engine that generates an estimated delivery time for the order, wherein the machine learning engine is trained using characteristics from historical orders. The similarity-based feature for the order can be calculated using a delivery time value of historical orders in a given order cluster where the order was assigned based on a predefined distance metric between the order and features of each order cluster. The proximity-based feature for the order can be calculated using a delivery time value of the historical orders that satisfy one or more predefined recency criteria.

Data driven estimation of order delivery date

Techniques are provided for anomaly-based ransomware detection of encrypted files. One exemplary method comprises obtaining metadata for an encrypted file; applying an anomaly detection technique to the metadata to compare at least one attribute in the metadata to one or more corresponding historical baseline values for the at least one attribute; and determining whether the encrypted file comprises a ransomware encryption based on the comparison. In some embodiments, one or more of file extension attributes, file size attributes and file name attributes in the metadata are compared to the one or more corresponding historical baseline values to identify a ransomware attack.

Anomaly-based ransomware detection for encrypted files

A method, comprising: receiving a first search query that is associated with a video file; retrieving one or more search results in response to the first search query, each of the search results corresponding to a different section in the video file; and displaying the search results on a display device, wherein displaying any of the search results includes displaying a link that points to the section of the video file, which corresponds to the search result.

Method and apparatus for presenting search results

Techniques are provided for basic input/output system (BIOS) protection using multi-factor authentication (MFA) based on digital identity values. One method comprises obtaining, by a BIOS of a hardware device, from a user device, (i) a request to access the BIOS, and (ii) a token based on a digital identity value for the user device; providing the token to an MFA chip on the hardware device, wherein the MFA chip evaluates the token and provides a verification result to the BIOS; and allowing the user device to access the BIOS based on the verification result. The digital identity value for the user device may be stored by the MFA chip during a fabrication of the MFA chip and/or a registration of the user device. The MFA chip may compare the digital identity value from the token received from the BIOS with the digital identity value for the user device stored by the MFA chip.

Basic input/output system protection using multi-factor authentication based on digital identity values

Techniques are provided for detecting malicious software code embedded in image files, using machine learning. One method comprises obtaining metadata for an image file; applying the obtained metadata to at least one machine learning technique to classify the image file into at least one of a plurality of predefined classes, wherein the plurality of predefined classes comprises at least one malicious file class; and determining whether the image file comprises malicious software code based on the classification. The machine learning technique can be trained using image files classified into at least one of the plurality of predefined classes. The machine learning technique may employ a deep neural network and/or a convolutional neural network to classify the image file into the at least one predefined class.

Detecting and mitigating malicious software code embedded in image files using machine learning techniques

A customer blockchain data store is provided. An exemplary method comprises obtaining a blockchain associated with a given customer of an enterprise having multiple customer communication channels, wherein the blockchain comprises transaction data for the given customer with the customer communication channels; obtaining new transaction data for the given customer for a given one of the customer communication channels; providing the new transaction data for the given customer to additional customer communication channels; receiving a validation of the new transaction data from the additional customer communication channels based on one or more predefined validation criteria; and storing the validated new transaction data for the given customer in the blockchain associated with the given customer. The validation optionally comprises the additional customer communication channels verifying information about the given customer in the new transaction data and/or aligning a customer identifier across the plurality of customer communication channels.

Enterprise storage of customer transaction data using a blockchain

A kernel-based proactive engine can be configured to evaluate system call functions that are invoked when user-mode objects make system calls. As part of evaluating a system call function, the kernel-based proactive engine can generate a feature vector for the system call function. The kernel-based proactive engine can then analyze the feature vector using a multidimensional anomaly detection algorithm that has been trained using feature vectors of system call functions that are known to be safe. When the evaluation indicates that the feature vector is anomalous, the kernel-based proactive engine can block the system call.

Kernel-based proactive engine for malware detection

A method, system and computer-usable medium are disclosed for voice-based alerting of an individual wearing an obstructive listening device. Certain embodiments include detecting speech in an ambient environment in which the person wearing the obstructive listening device is present; determining whether the detected speech includes a name of the person wearing the obstructive listening device; if the detected speech includes the name of the person wearing the obstructive listening device determining whether the name of the person was spoken using a calling speech characteristic; and if the name of the person was spoken using a calling speech characteristic, automatically alerting the person wearing the obstructive listening device that another person in the ambient environment is calling for attention of the person wearing the obstructive listening device.

System for voice-based alerting of person wearing an obstructive listening device

Techniques are provided for automatically detecting disruptive orders for a supply chain. One method comprises obtaining a quote for an order; extracting features from the quote; and applying the extracted features to a disruptive quote machine learning engine that generates an anomaly score indicating a likelihood that the quote will cause a disruption, based on one or more predefined disruption criteria. The disruptive quote machine learning engine may employ an isolation forest algorithm and/or a multi-dimensional anomaly detection algorithm. The disruptive quote machine learning engine may be trained using historical order information comprising part-level information from historical orders and/or a manufacturing production plan comprising an inventory forecast.

Automatic detection of disruptive orders for a supply chain

Life cycle management techniques are provided for cloud-based application executors with key-based access to other devices. An exemplary method comprises determining that a retention time for a first cloud-based application executor (e.g., a virtual machine or a container) has elapsed, wherein the first cloud-based application executor has key-based access to at least one other device using a first key; in response to the determining, performing the following steps: creating a second cloud-based application executor; and determining a second key for the second cloud-based application executor that is different than the first key, wherein the second cloud-based application executor uses the first key to add the second key to one or more trusted keys of the at least one other device and deactivates the first key from the one or more trusted keys.

Life cycle management for cloud-based application executors with key-based access to other devices

A method includes monitoring data of one or more containers running on one or more container host devices, a given one of the containers providing operating-system level virtualization for running at least one application. The method also includes determining a first set of behavior metrics for the given container based on the monitoring data, the first set of behavior metrics characterizing current behavior of the given container. The method further includes generating a model characterizing normal operation of the at least one application running in the given container using a second set of behavior metrics obtained during a learning period, utilizing the model to detect one or more anomalies in the first set of behavior metrics characterizing the current behavior of the given container, generating an alert responsive to detecting one or more anomalies in the first set of behavior metrics, and delivering the alert to a client device.

Monitoring containers running on container host devices for detection of anomalies in current container behavior

Techniques are provided for data-driven ensemble-based malware detection. An exemplary method comprises obtaining a file; extracting metadata from the file; obtaining a plurality of malware detection procedures; selecting a subset of the plurality of malware detection procedures to apply to the file utilizing a likelihood that each of the plurality of malware detection procedures will result in a malware detection for the file based on the extracted metadata; applying the selected subset of the malware detection procedures to the file; and processing results of the subset of malware detection procedures using a machine learning model to determine a probability of the file being malware.

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Beer Sheva, Israel

Or Herman Saffar