公开(公告)号：US20220141251A1

公开(公告)日：2022-05-05

申请号：US17083928

申请日：2020-10-29

Applicant: Booz Allen Hamilton Inc.

Inventor： Luke Edward RICHARDS ,  Andre Tai NGUYEN ,  Ryan Joseph CAPPS ,  Edward Simon Paster RAFF

IPC: H04L29/06 ,  G06K9/62 ,  G06N20/00

Abstract: A system and method for transferring an adversarial attack involving generating a surrogate model having an architecture and a dataset that mirrors at least one aspect of a target model of a target module, wherein the surrogate model includes a plurality of classes. The method involves generating a masked version of the surrogate model having ewer classes than the surrogate model by randomly selecting at least one class of the plurality of classes for removal. The method involves attacking the masked surrogate model to create a perturbed sample. The method involves generalizing the perturbed sample for use with the target module. The method involves transferring the perturbed sample to the target module to alter an operating parameter of the target model.

公开(公告)号：US20210133513A1

公开(公告)日：2021-05-06

申请号：US17130254

申请日：2020-12-22

Applicant: Booz Allen Hamilton Inc.

Inventor： Arash RAHNAMA-MOGHADDAM ,  Andre Tai NGUYEN

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/10 ,  G06N3/04 ,  G06N20/20

Abstract: An exemplary device for classifying an image includes a receiving unit that receives image data. The device also includes a hardware processor including a neural network architecture to extract a plurality of features from the image data, filter each feature extracted from the image data, concatenate the plurality of filtered features to form an image vector, evaluate the plurality of concatenated features in first and second layers of a plurality of fully connected layers of the neural network architecture based on an amount of deviation in the features determined at each fully connected layer, and generate a data signal based on an output of the plurality of fully connected layers. A transmitting unit sends the data signal to a peripheral or remote device.

公开(公告)号：US20210406309A1

公开(公告)日：2021-12-30

申请号：US17343474

申请日：2021-06-09

Applicant: Booz Allen Hamilton Inc.

Inventor： Andre Tai NGUYEN ,  Luke Edward RICHARDS ,  Edward Simon Paster RAFF

IPC: G06F16/906 ,  G06K9/62

Abstract: A method and system for cross-modal manifold alignment of different data domains includes determining for a shared embedding space a first embedding function for data of a first domain and a second embedding function for data of a second domain using a triplet loss, wherein triplets of the triplet loss include an anchor data point from the first, a positive and a negative data point from the second domain; creating a first mapping for the data of the first domain using the first embedding function in the shared embedding space; creating a second mapping for the data of the second domain using the second embedding function in the shared embedding space; and generating a cross-modal alignment for the data of the first domain and the data of the second domain.

公开(公告)号：US20200304535A1

公开(公告)日：2020-09-24

申请号：US16814689

申请日：2020-03-10

Applicant: Booz Allen Hamilton Inc.

Inventor： Aaron SANT-MILLER ,  Andre Tai NGUYEN ,  William Hall BADART ,  Sarah OLSON ,  Jesse SHANAHAN

IPC: H04L29/06 ,  G06N20/00

Abstract: A method for detecting and/or identifying a cyber-attack on a network can include segmenting the network using a segmentation method with machine learning to generate one or more network segments; assigning a score to a data point within each network segment based on a presence or absence of an identified anomalous behavior of the data point; analyzing network data flow, via behavioral modeling, to provide a context for characterizing the anomalous behavior; combining, via a reinforcement learning agent, outputs of the segmentation method with behavioral modelling and assigned score to detect and/or identify a cyber-attack; providing one or more alerts to an analyst; receiving an analyst assessment of an effectiveness of the detection and/or identification; and providing the analyst assessment as feedback to the reinforcement learning agent.

公开(公告)号：US20200286001A1

公开(公告)日：2020-09-10

申请号：US16536926

申请日：2019-08-09

Applicant: Booz Allen Hamilton Inc.

Inventor： Andre Tai NGUYEN ,  Edward RAFF

IPC: G06N20/10 ,  G06N7/00 ,  G06N20/20

Abstract: A computer-implemented method for generating an interpretable kernel embedding for heterogeneous data. The method can include identifying a set of base kernels in the heterogeneous data; and creating multiple sets of transformed kernels by applying a unique composition rule or a unique combination of multiple composition rules to the set of base kernels. The method can include fitting the multiple sets into a stochastic process model to generate fitting scores that respectively indicate a degree of the fitting for each of the multiple sets; storing the fitting scores in a matrix; and standardizing the matrix to generate the interpretable kernel embedding for the heterogeneous data.