Proofpoint, Inc.

Brian Sanford Jones

Zachary Mitchell Abzug

Jeremy Thomas Jordan

Giorgi Kvernadze

Dallan Quass

Daniel Wallace Rapp

Spencer Koehler

Kevin Patrick Blissett

Alan Wessman

Andre Turgeon

Xinzi Wu

Dalian Quass

Michael Paul Jones

Aspects of the disclosure relate to generating threat intelligence information. A computing platform may receive forensics information corresponding to message attachments. For each message attachment, the computing platform may generate a feature representation. The computing platform may input the feature representations into a neural network, which may result in a numeric representation for each message attachments. The computing platform may apply a clustering algorithm to cluster each message attachments based on the numeric representations, which may result in clustering information. The computing platform may extract, from the clustering information, one or more indicators of compromise indicating that one or more attachments corresponds to a threat campaign. The computing platform may send, to an enterprise user device, user interface information comprising the one or more indicators of compromise, which may cause the enterprise user device to display a user interface identifying the one or more indicators of compromise.

Using neural networks to process forensics and generate threat intelligence information

Aspects of the disclosure relate to detecting and identifying malicious sites using machine learning. A computing platform may receive a uniform resource locator (URL). The computing platform may parse and/or tokenize the URL to reduce the URL into a plurality of components. The computing platform may identify human-engineered features of the URL. The computing platform may compute a vector representation of the URL to identify deep learned features of the URL. The computing platform may concatenate the human-engineered features of the URL to the deep learned features of the URL, resulting in a concatenated vector representation. By inputting the concatenated vector representation of the URL to a URL classifier, the computing platform may compute a phish classification score. In response to determining that the phish classification score exceeds a first phish classification threshold, the computing platform may cause a cybersecurity server to perform a first action.

Uniform resource locator classifier and visual comparison platform for malicious site detection

Aspects of the disclosure relate to message compliance analysis. A computing platform accesses historical messages. The computing platform pre-processes the historical messages to configure the historical messages for use in training a disclaimer model to identify whether or not input messages include a disclaimer. The computing platform trains, using the pre-processed historical messages, the disclaimer model. The computing platform receives a new message. The computing platform inputs, into the disclaimer model, the new message, which produces a disclaimer score indicating a likelihood that the new message includes a disclaimer. The computing platform compares the disclaimer score to a disclaimer threshold. Based on identifying that the disclaimer score meets or exceeds the disclaimer threshold, the computing platform removes, from a set of messages scheduled for compliance review, the new message, and sends, to an intended recipient of the new message, the new message.

Message compliance scanning and processing system

Aspects of the disclosure relate to detecting and identifying malicious sites using machine learning. A computing platform may receive image data of a graphical rendering of a resource available at a uniform resource locator (URL). The computing platform may compute a computer vision vector representation of the image data. The computing platform may compare the computer vision vector representation of the image data to stored numeric vectors representing page elements, resulting in a feature indicating whether the computer vision vector representation of the image data is visually similar to a known page element, and may input the feature to a classifier. The computing platform may receive, from the classifier, a phish classification score indicating a likelihood that the URL is malicious. In response to determining that the phish classification score exceeds a first phish classification threshold, the computing platform may cause a cybersecurity server to perform a first action.

The remote-controlled collapsible valet stand may assist a user in wearing an article of clothing without needing to reach behind themselves to grasp the clothing. The device may comprise a linear actuator mounted to a base and raising a plurality of arms to dress the user. The device may be remote controlled, and the user of the device may place an article of clothing onto the arms and then control the height of the actuator to have the clothing lifted onto their person for easy wearing. The remote-controlled collapsible valet stand may further comprise a plurality of clips allowing the clothing to be quickly disconnected once the user is wearing it, and height adjustability so that users of varying heights may be able to use the device. In one embodiment the device may comprise a plurality of cowlings, near field communication, audible and visual alerts, proximity sensors, and various attachments considered related in the art.

Remote-controlled collapsible valet stand

Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.

Using a machine learning system to process a corpus of documents associated with a user to determine a user-specific and/or process-specific consequence index

Golf towel

Uniform resource locator classifier and visual comparison platform for malicious site detection preliminary

An example towel may include a main body and a scrubbing element coupled to a first side of the main body. The main body may include a first material. The scrubbing element may include a second material that may be different from the first material.

Growing community of inventors

Cary, NC, United States of America

Brian Sanford Jones