Mcafee, Inc.

Musarubra US LLC

Mcaffe, LLC

Jonathan B King

Sherin Mary Mathews

Carl D Woodward

Celeste R Fralick

Steven L Grobman

Kunal Mehta

Eric Donald Wuehler

Yonghong Huang

Andrew V Holtzmann

Amit Kumar

German Lancioni

Arthur S Zeigler

David M Durham

Robert L Vaughn

Cedric Cochin

Venkata Ramanan Sambandam

Dmitri Rubakha

Amanda House

Utkarsh Verma

Igor Polevoy

James Douglas Bean

Sorcha Bairbre Healy

Gerard Donal Murphy

Carl W Woodward

Niall Fitzgerald

Christiaan Beek

Torry B Campbell

Antoine Bouchard

Celeste Fralick

Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes at least one memory, instructions, and processor circuitry to execute the instructions. The processor circuitry executes the instructions to identify a test data distribution, generate a first visualization of the identified test data distribution, select a visualization type for a machine learning model, generate a second visualization including an indication of features extracted from the test data by the machine learning model, and generate a third visualization of results of inference performed by the machine learning model, the inference performed on the test data.

Methods and apparatus to visualize machine learning based malware classification

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

There is disclosed a computer-implemented system and method of analyzing a batch of objects, including bucketizing the batch of objects into a plurality of buckets according to a feature of the objects; for objects within a batch, performing malware analysis on the objects to assign a malware analysis score, and adjusting the malware analysis score based on the batch; and performing respective security actions on the objects within the batch, based on the adjusted malware analysis score.

Multi-dimensional malware analysis

Methods, apparatus, systems, and articles of manufacture are disclosed that augment classification for low prevalence samples. An example non-transitory computer readable medium comprises instructions that, when executed, causes a machine to at least classify a data sample using a first classifier, classify the data sample using a second classifier different from the first classifier, the second classifier using a plurality of sensitive hashing (LSH) forests to analyze a sorted plurality of neighbor samples, determine whether a first classification result of the first classifier meets or exceeds a confidence threshold, in response to the first classification result of the first classifier meeting or exceeding the confidence threshold, output the first classification result, and in response to the first classification result of the first classifier not meeting or exceeding the confidence threshold, output a second classification result of the second classifier.

Methods and apparatus to augment classification coverage for low prevalence samples through neighborhood labels proximity vectors

There is disclosed in one example a computing apparatus, including: a hardware platform, including a processor, a memory, and a network interface; a bucketized reputation modifier table; and instructions encoded within the memory to instruct the processor to: perform a feature-based malware analysis of an object; assign the object a malware reputation according to the feature-based malware analysis; query and receive via the network interface a complementary score for a complementary property of the object; query the bucketized reputation modifier table according to the complementary score to receive a reputation modifier for the object; adjust the object's reputation according to the reputation modifier; and take a security action according to the adjusted reputation.

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

Methods, apparatus, systems and articles of manufacture are disclosed for generic process chain entity mapping. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to receive process chain input data, the input data including a system path, identify a match between a path alias and the input data, wherein the path alias includes an alias for one or more system path format patterns, extract at least one of (1) metadata information or (2) command line parameter information from the match, and output transformed data based on the at least one of the extracted metadata information or command line parameter information, the transformed data output in a generalized format.

Methods and apparatus for generic process chain entity mapping

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 and apparatus for secure software defined storage are disclosed. An example apparatus includes memory and a processor to access a read request for data written to a software defined storage location, obtain the requested data from the software defined storage location, perform a classification operation on the requested data to obtain classification data corresponding to the requested data, the classification data to represent whether the requested data includes personally identifiable information, in response to determining that the requested data includes personally identifiable information, apply data loss prevention to the requested data to create response data, determine whether a client requesting the data from the software defined storage location is authorized to access the requested data, and in response to determining that the client requesting data is authorized to access the requested data, transmit the response data to the client.

Secure software defined storage

Methods, apparatus, systems, and articles of manufacture are disclosed to securely audit communications. An example apparatus includes a participant list generator to, responsive to a command to provision a secured group of devices in a network to prevent malicious activity, generate a participant device list including one or more endpoint devices and a control plane server; a privilege controller to, based on a policy indicated in the command, set read and write privileges for the one or more endpoint devices and the control plane server; a command controller to, based on the command, determine whether to generate a shared communication key using a shared system key; and a communication processor to encrypt communications between the one or more endpoint devices and the control plane server using the shared communication key.

Methods, apparatus, and articles of manufacture to securely audit communications

There is disclosed in one example a computer-implemented method of detecting a statistically-significant security event and automating a response thereto, including: querying, or causing to be queried, a security intelligence database for sector-wise historical norms for an indicator of compromise (IoC); obtaining sector-wise expected prevalence data for the IoC; receiving observed sector-wise prevalence data for the IoC; computing a first test statistic from a goodness-of-fit test between the observed and expected prevalences; from the observed sector-wise prevalence data, computing a second test statistic from a difference between a highest prevalence and a next-highest prevalence; computing a third test statistic from a difference between the observed prevalence of a highest prevalence sector and the expected prevalence for the highest prevalence sector; selecting a least significant statistic from among the first, second, and third test statistics; and determining from the least significant statistic whether to notify a subscriber.

Skewness in indicators of compromise

A method including receiving a feature vector of an unknown sample, computing a MinHash of the unknown sample based on Jaccard-compatible features, querying a Locality Sensitive Hashing forest of known samples with the MinHash of the unknown sample to identify a first subset of known samples that are similar to the unknown sample, receiving for each individual known sample in the first subset, a feature vector including non-Jaccard distance-compatible features, computing a first sub-distance and a second sub-distance between the unknown sample and the known samples in the first subset, calculating a total distance for each known sample in the first subset by combining the first and the second sub-distances, identifying, based on the calculated total distances, a second subset of known samples that are most similar to the unknown sample, and classifying the unknown sample based on the second subset.

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Forest Grove, OR, United States of America

Jonathan B King