Symantec Corporation

Nortonlifelock Inc.

Gen Digital Inc.

Ca, Inc.

Kevin Alejandro Roundy

Christopher Gates

Sandeep B Bhatkar

Acar Tamersoy

Michael Andrew Hart

Fanglu Guo

Chris Gates

Anand Kashyap

Daniel Kats

Leylya Yumer

Yufei Han

Aleatha Parker-Wood

Matteo Dell'Amico

Yin Liu

Daniel Marino

Jie Fu

Tao Cheng

Zhi Kai Li

Petrus Johannes Viljoen

Mahmood Sharif

Michael Spertus

David Luz Silva

Sanjay Sawhney

Saurabh Shintre

Stanislav Miskovic

Nikolaos Vasiloglou

Elias Khalil

Shang-Tse Chen

Bo Li

Yining Wang

Sourabh Satish

Yun Shen

Susanta Kumar Nanda

Scott D Schneider

Sharada Sundaram

Brian T Witten

Michael Rinehart

Yuqiong Sun

Xiaolin Wang

Darren Shou

Olivier Thonnard

Ashwin Kumar Kayyoor

Wangyan Feng

Felix Leder

Ningwei Liu

Shuning Wu

Leylya Bilge

Dong Chung

Ying Li

Lars Haukli

Kyle Soska

Platon Kotzias

Adam Overfield

Arif Shaikh

Yuzhe Ma

Anil Sharma

The disclosed computer-implemented method for utilizing multiple analysis techniques to distinguish stalkerware from privacy-invasive application types may include (i) inspecting application package data associated with a target application, (ii) analyzing application marketplace data for the target application to determine descriptions of potential privacy-invasive actions, (iii) predicting, utilizing a program machine learning classifier, sensitive data leaks associated with the target application based on the application package data, (iv) predicting, utilizing a marketplace machine learning classifier, surveillance activities associated with the target application based on the descriptions of the potential privacy-invasive actions, and (v) performing a security action that assigns a privacy invasion category to the target application to distinguish the target application from stalkerware and other privacy invasion application types. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for utilizing multiple analysis techniques to distinguish stalkerware from privacy-invasive application types

The disclosed computer-implemented method for detecting malware based on anomalous cross-customer financial transactions may include (i) detecting, using a machine-learning algorithm, a set of anomalies associated with fraudulent financial transactions for source user accounts in a group of customer financial accounts, (ii) identifying, based on customer transaction metadata associated with a group of target user accounts in the customer financial accounts, a cluster of financial transactions having anomaly instances in common with the set of anomalies, (iii) linking each of the customer financial accounts having the common anomaly instances in the cluster of financial transactions with a corresponding customer threat protection account to discover a user device identification, (iv) determining that artifacts appearing on a group of user devices are associated with a potential malware attack, and (v) performing a security action that protects against the potential malware attack. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for detecting malware based on anomalous cross-customer financial transactions

Location-based anomaly detection based on geotagged digital photographs. In some embodiments, a method may include identifying a completed transaction associated with a user. The method may also include determining a transaction geographic location associated with the completed transaction. The method may further include identifying a mobile device associated with the user. The method may also include identifying one or more geotagged digital photographs taken by the mobile device. The method may further include extracting one or more photograph geographic locations from the one or more geotagged digital photographs. The method may also include, in response to determining that the transaction geographic location is not within a threshold distance of any of the one or more photograph geographic locations, identifying the completed transaction as a suspicious transaction and performing a remedial action.

Location-based anomaly detection based on geotagged digital photographs

A computer-implemented method for protecting data on devices may include (i) identifying a device that is operated by a user and that comprises private data pertaining to the user, (ii) determining that stalkerware on the device is sending the private data to an unauthorized device not operated by the user, (iii) requesting, in response to determining that the stalkerware is sending the private data to the unauthorized device, that the user select at least one safety plan step from a set of safety plan options, and (iv) modifying, at least in part based on the safety plan step selected by the user, outgoing data sent by the stalkerware to the unauthorized device. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for protecting data on devices

The disclosed computer-implemented method for utilizing user identity notifications to protect against potential privacy attacks on mobile devices may include (i) monitoring a mobile computing device to detect one or more user interactions by a current user, (ii) identifying the current user of the mobile computing device, (iii) determining that the current user is a potentially malicious user associated with one or more privacy-invasive applications installed on the mobile computing device, and (iv) performing a security action that protects a benign user of the mobile computing device against an attack initiated by the potentially malicious user associated with the privacy-invasive applications. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for utilizing user identity notifications to protect against potential privacy attacks on mobile devices

The disclosed computer-implemented method for protecting search privacy may include (i) receiving, via a search interface, a search query comprising at least one search term, (ii) determining a sensitivity level of the search query based on the at least one search term, (iii) directing the search query to a search engine that has a level of privacy correlated with the sensitivity level of the search query, and (iv) returning, via the search interface, at least one result of directing the search query to the search engine that has the level of privacy correlated with the sensitivity level of the search query. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for protecting search privacy

The disclosed computer-implemented method for detecting inter-personal attack applications may include (i) receiving application marketplace information describing application feature information, (ii) creating, by performing natural language processing on the feature information, a feature vector identifying a potentially malicious functionality of the application, (iii) creating a profiling vector that is a categorical feature representation of installation information from an application installation file, and (iv) performing a security action including (A) mapping, using a machine learning model, the feature vector and the profiling vector to a multi-dimensional output vector having element corresponding to a malware category and (B) determining a malicious extent of the application by combining the categories identified by the multi-dimensional output vector with bi-partite graph information identifying (I) relations between a plurality of applications and (II) relations between a plurality of computing devices hosting the plurality of applications. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for detecting inter-personal attack applications

Identifying and protecting against malicious apps installed on client devices. In some embodiments, a method may include (a) identifying client devices, (b) identifying apps installed on the client devices, (c) assigning each of the apps known to be a malicious app with a highest app suspicion score, (d) assigning each of the other apps as an unknown app with a lowest app suspicion score, (e) assigning each of the client devices with a device suspicion score, (f) assigning each of the unknown apps with an updated app suspicion score, (g) repeating (e), and repeating (f) with a normalization, until the device suspicion scores and the app suspicion scores converge within a convergence threshold, (h) identifying one of the unknown apps as a malicious app, and (i) protecting against the malicious app by directing performance of a remedial action to protect the client device from the malicious app.

Identifying and protecting against surveillance apps installed on client devices

The disclosed computer-implemented method for customizing security alert reports may include (i) identifying a local machine learning model that predicts how a client responds to security alerts generated for the client, (ii) identifying a set of peer machine learning models that predict how a set of peers of the client each responds to security alerts generated for each respective peer, (iii) measuring a level of similarity between the client and each respective peer of the set of peers according to a similarity metric to create a similarity model, (iv) aggregating the local machine learning model and at least one of the set of peer machine learning models based on the similarity model to create an aggregated machine learning model, and (v) protecting the client by applying the aggregated machine learning model to customize an electronically displayed security alert report. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for customizing security alert reports

The disclosed computer-implemented method for training malware classifiers may include (1) perturbing, at a computing device, a binary file in a manner that maintains functionality of the binary file, (2) classifying the perturbed binary file with a first machine learning classifier to produce a classification result, (3) producing a transformed file by repeating the perturbing and classifying steps until the transformed file becomes misclassified, and (4) performing a security action comprising training a second machine learning classifier with the transformed file and an associated correct classification result. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for training malware classifiers

Protecting against an impersonation scam in a live video stream. In some embodiments, a method may include periodically extracting and storing signature features from verified video streams of verified streamers, identifying an unverified live video stream of an unverified streamer being viewed by one or more users, extracting and storing signature features from the unverified live video stream, computing overall distance scores between the signature features of the unverified live video stream and the signature features of the verified video streams, determining whether the unverified streamer is impersonating one or more of the verified streamers by determining whether one or more of the overall distance scores are less than a distance threshold, determining whether one or more text signature features of the unverified live video stream include an impersonation scam, and performing a remedial action to protect the one or more users from the impersonation scam.

Protecting against an impersonation scam in a live video stream

The present disclosure relates to using correlations between support interaction data and telemetry data to discover emerging incidents for remediation. One example method generally includes receiving a corpus of support interaction data and a corpus of telemetry data. Topics indicative of underlying problems experienced by users of an application are extracted from the corpus of support interaction data. A topic having a rate of appearance in the support interaction data above a threshold value is identified. A set of telemetry data relevant to the topic is extracted from the corpus of telemetry data, and a subset of the relevant set of telemetry data having a frequency in the relevant set of telemetry data above a second threshold value is identified. The topic and the subset of telemetry data are correlated to an incident to be remediated, and one or more actions are taken to remedy the incident.

Discovery of computer system incidents to be remediated based on correlation between support interaction data and computer system telemetry data

The disclosed computer-implemented method for preparing honeypot computer files may include (1) identifying, at a computing device, a search term used by a cyber attacker in an electronic search request, (2) identifying, without regard to a search access restriction, a sensitive computer document in search results stemming from the electronic search request, (3) creating, as a security action in response to the electronic search request, a honeypot computer file based on the sensitive computer document and including the identified search term, and (4) placing the honeypot computer file in the search results. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for preparing honeypot computer files

The disclosed computer-implemented method for preventing decentralized malware attacks may include (i) receiving, by a computing device, node data from a group of nodes over a network, (ii) training a machine learning model by shuffling the node data to generate a set of outputs utilized for predicting malicious data, (iii) calculating a statistical deviation for each output in the set of outputs from an aggregated output for the set of outputs, and (iv) identifying, based on the statistical deviation, an anomalous output in the set of outputs that is associated with one or more of the malicious nodes, the one or more malicious nodes hosting the malicious data. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for preventing decentralized malware attacks

A method for improving memory efficiency of production rule systems is described. In one embodiment, the method includes identifying a rule associated with production rule systems, constructing a production rule network based at least in part on the rule, identifying a positional constraint associated with the rule, and implementing an alpha memory gate in the production rule network based at least in part on the positional constraint. In some cases, the alpha memory gate is one of a plurality of nodes of the production rule network.

Memory efficiency of production rule systems

Growing community of inventors

El Segundo, CA, United States of America

Kevin Alejandro Roundy