Mcafee, Inc.

Celeste R Fralick

Sherin Mary Mathews

Carl D Woodward

Jonathan B King

Kunal Mehta

Yonghong Huang

Amanda House

German Lancioni

John Wagener

Joanna Negrete

Rahul Deshpande

Armando Rodriguez

Adam Wosotowsky

Eric Peterson

Raj Vardhan

Lynda M Grindstaff

Andrew V Holtzmann

Sandeep Chandana

Dattatraya Kulkarni

Srikanth Nalluri

Gabriel G Infante-Lopez

Eric Donald Wuehler

Steve Grobman

Utkarsh Verma

Sonam Bothra

Andres More

Shivangee Trivedi

Steve Povolny

There is disclosed in one example a ransomware mitigation engine, including: a processor; a convolutional neural network configured to provide file type identification (FTI) services including: identifying an access operation of a file as a write to the file or newly creating the file; computing a byte correlation factor for the file; classifying the file as belonging to a file type; determining with a screening confidence that the file type is correct for the file; determining that the screening confidence is below a screening confidence threshold; and circuitry and logic to provide heuristic analysis including: receiving notification that the confidence is below the confidence threshold; performing a statistical analysis of the file to determine a difference between an expected value and a computed value; determining from the difference, with a detection confidence, that the file has been compromised; and identifying the file as having been compromised by a ransomware attack.

Detecting ransomware

Security risk evaluation across user devices is disclosed herein. An example method includes identifying a user and one or more devices associated with the user, collecting information identifying applications used by the user on the one or more devices, determining respective security sub-scores for each item of the one or more devices, computing an overall security score for the user based, at least in part, on an aggregation of the security sub-scores, and creating a user profile based on the overall security score, the user profile to enable the at least one of the one or more devices to exchange data with an external device when the overall security score meets a security score threshold, the user profile to prevent the at least one of the one or more devices from exchanging data with the external device when the overall security score does not meet the security score threshold.

Security risk evaluation across user devices

Methods, apparatus, systems and articles of manufacture to improve deepfake detection with explainability are disclosed. An example apparatus includes a deepfake classification model trainer to train a classification model based on a first portion of a dataset of media with known classification information, the classification model to output a classification for input media from a second portion of the dataset of media with known classification information; an explainability map generator to generate an explainability map based on the output of the classification model; a classification analyzer to compare the classification of the input media from the classification model with a known classification of the input media to determine if a misclassification occurred; and a model modifier to, when the misclassification occurred, modify the classification model based on the explainability map.

Methods and apparatus to improve deepfake detection with explainability

A method for halting malware includes: monitoring plural file system events with a system driver to detect an occurrence of a file system event having a predetermined file type and log event type; triggering a listening engine for file system event stream data of a file associated with the detection of the file system event, the file system event stream data indicating data manipulation associated with the file due to execution of a process; obtaining one or more feature values for each of plural different feature combinations of plural features of the file based on the file system event stream data; inputting one or more feature values into a data analytics model to predict a target label value based on the one or more feature values of the plural different feature combinations and agnostic to the process; and performing a predetermined operation based on the target label value.

Model development and application to identify and halt malware

Methods, apparatus, systems and articles of manufacture to defend against adversarial machine learning are disclosed. An example apparatus includes memory; computer readable instructions; and processor circuitry to execute the computer readable instructions to: generate a first output indicating a feature that contributed to the generation of a classification by a machine learning model; compare the first output with a second output generated by a server that trained the machine learning model; and flag the machine learning model as corresponding to at least one of model drift or an adversarial attack when first output differs from the second output by more than a threshold.

Methods and apparatus to defend against adversarial machine learning

Methods, apparatus, systems and articles of manufacture for producing generic Internet Protocol (IP) reputation through cross-protocol analysis are disclosed. An example apparatus includes a data collector to gather a first data set representing IP telemetry data for a first protocol, the data collector to gather a second data set representing IP telemetry data for a second protocol different from the first protocol. A label generator is to generate a training data set based on records in the first data set and the second data set having matching IP addresses, the training data set to include combined label indicating whether each of the respective matching IP addresses is malicious. A model trainer is to train a machine learning model using the training data set. A model executor is to, responsive to a request from a client device, execute the machine learning model to determine whether a requested IP address is malicious.

Methods, systems, articles of manufacture and apparatus for producing generic IP reputation through cross protocol analysis

Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Methods and apparatus to detect deepfake content

A non-transitory computer readable medium includes computer executable instructions that, when executed, cause at least one processor to train a model to perform at least one of a prediction operation, a diagnostic operation, or a classification operation based on a training dataset, deploy the model in a production computer system to perform the at least one operation on field data, monitor signal data associated with the model, the signal data including specific or derived signal data representing characteristics of an ecosystem in which the model is deployed and new observations in incoming field data, monitor accuracy of the model by applying a statistical tool to a plurality of data points of the signal data, apply a secondary machine learning predictive engine to the plurality of data points of the signal data to predict future data points of the signal data, determine whether the signal data represents an unstable process by identifying future outlier data points from among the plurality of future data points of the signal data, select a rule corresponding to the future outlier data points, the rule to suggest at least one of a cause of the unstable process or an effect of the unstable process on the signal data, and generate an indication that a corrective action should be taken on the model based on a result of the determination, the indication to identify the rule and at least one of the cause of the unstable process or the effect of the unstable process on the signal data.

Automated and customized post-production release review of a model

Methods, apparatus, systems and articles of manufacture are disclosed to analyze network traffic for malicious activity. An example apparatus includes a graph generator to, in response to obtaining one or more internet protocol addresses included within input data, generate a graph data structure based on one or more features of the one or more internet protocol addresses in the input data, a file generator to generate a first matrix using the graph data structure, the first matrix to represent nodes in the graph data structure and generate a second matrix using the graph data structure, the second matrix to represent edges in the graph data structure, and a classifier to, using the first matrix and the second matrix, classify at least one of the one or more internet protocol addresses to identify a reputation of the at least one of the one or more internet protocol addresses.

Methods and apparatus to analyze network traffic for malicious activity

Methods, apparatus, systems, and articles of manufacture are disclosed that improve detection of malware based on ecosystem specific data. An example apparatus includes a feedback weight controller to apply, with a machine learning model, a weight to feedback associated with a sample, the feedback obtained from at least a customer ecosystem and including endpoint feedback, human feedback, infrastructure feedback, and global feedback; and a sample conviction controller to, in response to a score based on the weighted feedback satisfying a threshold for a classification, indicate to a user, with the machine learning model, that the classification for the sample is malicious.

Methods and apparatus to improve detection of malware based on ecosystem specific data

Methods, apparatus, systems and articles of manufacture to defend against adversarial machine learning are disclosed. An example apparatus includes a model trainer to train a classification model based on files with expected classifications; and a model modifier to select a convolution layer of the trained classification model based on an analysis of the convolution layers of the trained classification model; and replace the convolution layer with a tree-based structure to generate a modified classification model.

Example apparatus disclosed herein generate blocks of a blockchain, the blockchain to store a neural network that has input nodes, hidden nodes and output nodes, with respective ones of the blocks of the blockchain including respective code and respective data to represent corresponding ones of the output nodes of the neural network, a first one of the blocks including first code and first data to implement operations to be performed by a first one of the output nodes, the hidden nodes and the input nodes on input data applied to the neural network to determine an output of the first one of the output nodes. Disclosed example apparatus also train the neural network to determine at least portions of the respective data to include in the respective ones of the blocks of the blockchain, and forward the blockchain to a server that is to distribute the neural network to client(s).

Distribution of neural networks with blockchains

Security risk evaluation across user devices is disclosed herein. An example method includes registering one or more devices associated with a first user with the computer system, determining respective security sub-scores for each item of the one or more devices, computing an overall security score for the first user based, at least in part, on an aggregation of the security sub-scores, and creating a user profile based on the overall security score, the user profile to enable the at least one of the one or more devices to exchange data with an external device when the overall security score meets a security score threshold, the user profile to prevent the at least one of the one or more devices from exchanging data with the external device when the overall security score does not meet the security score threshold.

There is disclosed in one example a computer-implemented anti-ransomware method, including: selecting a file for inspection; assigning the file to a type class according to a file type identifier; receiving an expected byte correlation for the type class; computing, according to a byte distribution of the file, a byte correlation for the file; comparing, via statistical analysis, the byte correlation to the expected byte correlation; and determining that the file has been compromised, including determining that the file has a byte correlation that deviates from the expected byte correlation by more than a threshold, taking a ransomware remediation action for the file.

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Lubbock, TX, United States of America

Celeste R Fralick