Juniper Networks, Inc.

Wine Plum, Inc.

Juniper Neworks, Inc.

Mykonos Software, Inc.

Kyle Adams

Daniel J Quinlan

Jacob Asher Langton

Declan Conlon

Zhenxin Zhan

Oskar Ibatullin

David Andrew Koretz

Donald G Hubbard, Jr

Niculae Mustatea

Michael Dumont

Bryan Burns

Travis Ronald Coleman

Benjamin J Hafen

Raul E Chinda

Robert W Cameron

David Henry Caldwell Karl

Yuly Tenorio Morales

Victor Pinenkov

Raul Eugene Chinda

Jr Donald G Hubbard

Benjamin Jonas Hafen

Robert Cameron

A device may receive an instruction to classify software. The device may identify a group of one or more user interfaces associated with the software based on receiving the instruction to classify the software. The device may determine a group of one or more user interface signatures associated with the group of one or more user interfaces. A user interface signature may include information, associated with a user interface in the group of one or more user interfaces, that may be used to classify the software. The device may generate information that identifies a classification of the software based on the group of one or more user interface signatures and based on known signature information. The known signature information may include information that corresponds to a correct software classification. The device may output the information that identifies the classification of the software.

Classification of software based on user interface elements

A device may identify a plurality of files for a multi-file malware analysis. The device may execute the plurality of files in a malware testing environment. The device may monitor the malware testing environment for behavior indicative of malware. The device may detect the behavior indicative of malware. The device may perform a first multi-file malware analysis or a second multi-file malware analysis based on detecting the behavior indicative of malware. The first multi-file malware analysis may include a partitioning technique that partitions the plurality of files into two or more segments of files to identify a file, included in the plurality of files, that includes malware. The second multi-file malware analysis may include a scoring technique that modifies a plurality of malware scores, corresponding to the plurality of files, to identify the file, included in the plurality of files, that includes malware.

Multi-file malware analysis

A device may receive data from a first endpoint device. The device may identify a network protocol. The network protocol may be associated with receiving the data. The device may identify a format. The format may be associated with encoding textual information in the data. The device may determine, based on the format and the network protocol, text in the data. The device may determine whether the text includes a reference from a plurality of references. The plurality of references may identify addresses associated with malicious devices. The device may selectively forward the data to a second endpoint device based on determining whether the text includes the reference.

Filtering data using malicious reference information

A device may receive an instruction to automatically install a program using a click area prediction model. The click area prediction model may be associated with predicting a click area of a user interface that, when selected, causes a program installation procedure to proceed. The device may identify an installation user interface associated with installing the program. The device may determine a group of regions included in the installation user interface. The device may identify sets of features associated with the group of regions. The device may determine, based on the sets of features and the click area prediction model, a group of scores associated with the group of regions. The device may identify a particular region as a predicted click area based on the group of scores. The device may select the predicted click area to attempt to cause the program installation procedure to proceed.

Automated software installation using a click area prediction model

A liquid dispensing device includes a liquid extraction system that extracts a liquid from a container. The liquid dispensing device includes an identification system that identifies a type of the liquid. The liquid dispensing device includes a liquid preservation system that prevents degradation of the liquid. The liquid may be wine or other liquids.

Liquid dispensing device

A liquid dispensing device includes a needle. The needle is adapted to penetrate a container without tearing out a portion of the container. The portion of the container is near a point of penetration on the container. The liquid dispensing device includes a motion control system. The motion control system is configured to rotate the needle while the needle penetrates the container.

A device may determine that a file of a client device is a malicious file. The device may obtain remote access to the client device using a connection tool. The connection tool may provide access and control of the client device. The remote access may include access to a file location of the malicious file. The device may determine file information associated with the malicious file using the remote access to the client device. The device may select one or more remediation actions based on the file information. The device may cause the one or more remediation actions to be executed using the remote access to the client device.

Remote remediation of malicious files

A device may receive a trigger to determine whether a malicious file is operating on a client device. The device may determine a network activity profile associated with the malicious file based on receiving the trigger to determine whether the malicious file is operating on the client device. The network activity profile may include information regarding network activity associated with the malicious file when the malicious file is executed in a testing environment. The device may monitor network activity associated with the client device. The device may determine that the network activity associated with the client device matches the network activity profile associated with the malicious file based on monitoring the network activity associated with the client device. The device may provide information indicating that the network activity associated with the client device matches the network activity profile associated with the malicious file.

Detecting a malicious file infection via sandboxing

A device may receive a file to be analyzed in a sandbox environment, and may determine configuration information for configuring the sandbox environment. The configuration information may be determined based on at least one of: file information associated with the file to be analyzed, or client device information associated with a client device for which the file is intended. The device may configure the sandbox environment using the configuration information. The configuration information may identify a system configuration for the sandbox environment. The device may analyze the file in the sandbox environment based on configuring the sandbox environment using the configuration information.

Configuring a sandbox environment for malware testing

A system may determine to perform an internal and an external malware detection operation to detect a malware infection associated with a client device. The system may perform the internal operation by modifying an environment, executing on a particular device, to form a modified environment. The system may perform the external operation by performing a communication from the particular device. The system may monitor the modified environment for a first behavior indicative of the malware infection, and may monitor a result of performing the communication for a second behavior indicative of the malware infection. The system may detect that the first or second behavior has occurred. The system may provide a notification that the client device is infected with malware based on detecting that the first or second behavior has occurred. The notification may cause one or more network devices to block network traffic to or from the client device.

Malware detection using internal and/or external malware detection operations

Embodiments of the present disclosure are directed to extracting the liquid from a container (such as a bottle of wine) in varying amounts at a desired serving temperature while preserving the liquid for later consumption. Various embodiments may be built within an appliance so the consumer experience is fully automated. Embodiments may include a variety of wine preservation techniques including the use of inert gases that prevent the oxidation of wine.

Systems and methods for wine processing

A security device may receive actual behavior information associated with an object. The actual behavior information may identify a first set of behaviors associated with executing the object in a live environment. The security device may determine test behavior information associated with the object. The test behavior information may identify a second set of behaviors associated with testing the object in a test environment. The security device may compare the first set of behaviors and the second set of behaviors to determine a difference between the first set of behaviors and the second set of behaviors. The security device may identify whether the object is an evasive malicious object based on the difference between the first set of behaviors and the second set of behaviors. The security device may provide an indication of whether the object is an evasive malicious object.

Identifying an evasive malicious object based on a behavior delta

A client device may provide, to a host device, a request to access a website associated with a host domain. The client device may receive, based on the request, verification code that identifies a verification domain and a resource, associated with the verification domain, to be requested to verify a public key certificate. The verification domain may be different from the host domain. The client device may execute the verification code, and may request the resource from the verification domain based on executing the verification code. The client device may determine whether the requested resource was received, and may selectively perform a first action or a second action based on determining whether the requested resource was received. The first action may indicate that the public key certificate is not valid, and the second action may indicate that the public key certificate is valid.

Detecting and preventing man-in-the-middle attacks on an encrypted connection

A device may receive or generate a message for routing to a destination on a communication channel. The communication channel may have been established between a source and the destination. The device may perform a first determination of policy information related to at least one of the message, the destination, or the source of the message. The policy information may describe an action for a network device to perform. The device may associate a policy token with the message. The policy token may describe or identify the policy information. The device may provide the message with the associated policy token to the network device on the communication channel to cause the network device to perform a second determination of the policy information based on the policy token, to perform the action described by the policy information, and to provide the message on the communication channel.

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Brisbane, CA, United States of America

Kyle Adams