公开(公告)号：US20250045418A1

公开(公告)日：2025-02-06

申请号：US18790022

申请日：2024-07-31

Applicant: Booz Allen Hamilton Inc.

Inventor： Leander A. Metcalf II ,  Allen Stewart

IPC: G06F21/57

Abstract: Embodiments can relate to a system for automated exploit generation that receives input data representative of a target action to establish a target having a potential target vulnerability. The system can build a simulated target environment that includes the established target. The system can conduct an analysis method including a static, a concrete, a dynamic, and/or a symbolic analysis. The system can create a chainable sequence including an information disclosure, a read, a write, and/or an execution exploit primitive. The system can generate an exploit chain that, when executed by the processor in response to the target action, can transform the target action to a target failure within the simulated target environment and thereby expose the target vulnerability. The system can execute the exploit chain within the simulated target environment to examine coverage of the exposed target vulnerability. The system can generate an output representative of the exposed target vulnerability.

公开(公告)号：US20240303331A1

公开(公告)日：2024-09-12

申请号：US18475601

申请日：2023-09-27

Applicant: Booz Allen Hamilton Inc.

Inventor： Robert J. Joyce ,  Edward Simon Pastor Raff

IPC: G06F21/56 ,  G06N3/0442

CPC classification number: G06F21/561 ,  G06N3/0442 ,  G06F2221/034

Abstract: Provided are methods, systems, and non-transitory computer-readable media for generating a feature vector for malware, including storing, in memory of a computing device, program code for a trained neural network that produces embedded representations for antivirus scan data; executing, by a processor of the computing device, the program code for the trained neural network to perform the operations of: (a) receiving an antivirus scan report (AVSR) for a malware file; (b) normalizing each label in the AVSR by separating the label into a sequence of tokens including a set of token strings; (c) embedding a first token and plural second tokens to generate an input sequence for the malware file; (d) inputting the input sequence into a neural model for producing antivirus scan data; and (e) outputting the antivirus scan data produced by the neural model as one or more feature vectors.

公开(公告)号：US12061678B2

公开(公告)日：2024-08-13

申请号：US17494317

申请日：2021-10-05

Applicant: Booz Allen Hamilton Inc.

Inventor： Clayton Davis ,  Saumil Dave ,  Baruch Gutow ,  Gabriella Melki

IPC: G06F21/16

CPC classification number: G06F21/16

Abstract: Exemplary systems and methods are directed to embedding data into a machine learning model. A processing device executes program code for running a machine learning model, which has a plurality of parameter values. The processing device receives a message to be embedded into the machine learning model. The message is encrypted according to a set of keys of a cryptographic algorithm. The encrypted message is converted to a corresponding binary representation. The binary representation of the encrypted message is embedded into at least one of the one or more parameters of the machine learning model. The embedding operation modifies the at least one parameter value of the machine learning model.

公开(公告)号：US20240256963A1

公开(公告)日：2024-08-01

申请号：US18423774

申请日：2024-01-26

Applicant: Booz Allen Hamilton Inc.

Inventor： Edward Simon Paster Raff ,  Amol Ashish Khanna ,  Fred Sun Lu

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Exemplary systems and methods are directed to training a machine learning model and for preventing leakage of training data by the machine learning model subsequent to training. A processor is configured to convert a sparse dataset into a matrix of plural data coordinates, generate a priority queue populated with the plural data coordinates, and iteratively select a data coordinate from the priority queue. Plural model values are calculated such that any zero value in the sparse dataset is avoided while maintaining a same result. A next feature is selected, and its weight is altered. Plural variables of the matrix are updated based on the altered weight value, and the priority queue is updated to adjust a priority of the data coordinates based on the update to the plural variables. The process is repeated for each next data coordinate until the model converges to a solution based on the model weights.

公开(公告)号：US20240087297A1

公开(公告)日：2024-03-14

申请号：US18462941

申请日：2023-09-07

Applicant: Booz Allen Hamilton Inc.

Inventor： Luke SHELLHORN ,  Melodie BUTZ ,  Christopher NITHIANANDAM ,  Andrew MARTIN ,  Zachary HUMAYUN ,  Ryan CHAN

IPC: G06V10/774 ,  G06V20/40

CPC classification number: G06V10/774 ,  G06V20/49

Abstract: Exemplary systems and methods are directed to generating customized imagery includes receiving input parameters that define operations for one of plural disparate image processing tools in generating the customized imagery and define attributes of the customized imagery to be generated. Program code for generating an API is executed and the API establishes communication with each image processing tool. The API generates parameterized calls which provide instructions for a specified one of the image processing tools to generate the customized imagery. The image processing tool which receives the instructions is identified from the input parameters. The parameterized calls are sent to the parameterized calls to the image processing tool and the customized imagery is generated. The customized imagery is returned to the API and is stored in a database as training data for an artificial intelligence model.

公开(公告)号：US11892202B2

公开(公告)日：2024-02-06

申请号：US17951447

申请日：2022-09-23

Applicant: Booz Allen Hamilton Inc.

Inventor： Igor Vaisman ,  Joshua Peters

IPC: F25B1/10

CPC classification number: F25B1/10 ,  F25B2400/13

Abstract: Thermal management techniques include: transporting a refrigerant fluid from a receiver to an inlet of a flash tank that has a vapor-side outlet and liquid-side outlet such that a liquid phase of the refrigerant fluid moves to a bottom of the flash tank and outputs from the liquid-side outlet; forming a solid-vapor state from the liquid phase by expanding the liquid phase with an expansion valve to a first pressure that is less than a triple point pressure to form a solid-vapor mixture of the refrigerant fluid; extracting heat from a heat load with an evaporator that receives the solid-vapor mixture of the refrigerant fluid and sublimates the solid state of the solid-vapor mixture of the refrigerant fluid directly into a vapor phase of the refrigerant fluid; and discharging, from an exhaust line, the vapor phase to an ambient environment without returning the vapor phase to the receiver.

公开(公告)号：US20240028745A1

公开(公告)日：2024-01-25

申请号：US18356501

申请日：2023-07-21

Applicant: Booz Allen Hamilton Inc.

Inventor： Hannah Davies ,  Michael Saxton

IPC: G06F21/57 ,  G06F21/55

CPC classification number: G06F21/577 ,  G06F21/554

Abstract: Exemplary systems and methods are directed to endpoint detection and response (EDR) in which a receiver receives streaming data from plural EDR platforms with vendor-specific data formats for the streaming data. An application programming interface converts the streaming data received from each EDR platform to a common data format. A detection engine analyzes the converted streaming data for attributes of malicious activity and generates an alert when malicious activity is detected. A graphical user interface filters and sorts the generated alerts based on at least one of a priority of addressing the malicious activity and a severity of harm caused by the malicious activity. The graphical user interface further generates an interactive display of the filtered and sorted alerts, where each alert includes an active or activatable link which when selected provides additional information obtained from one of the plural EDR platforms associated with the alert.

公开(公告)号：US20230396639A1

公开(公告)日：2023-12-07

申请号：US18327596

申请日：2023-06-01

Applicant: Booz Allen Hamilton Inc.

Inventor： Alexandra Kay Greene ,  Kristopher Joseph Hall

IPC: H04L9/40 ,  H04L41/16

CPC classification number: H04L63/1425 ,  H04L41/16

Abstract: Exemplary systems and methods are directed to anomaly detection on a mobile communication network. A computing device receives data samples formatted from at least an endpoint and/or an interface on the mobile network. The computing device uses an encoder to build a data model of the mobile network data using data included in one or more data fields of each received data sample. The data model including datasets that meet at least one of a security or performance specification. A set of detectors is generated from the data model by performing a negative selection to select one or more datasets that do not match data that meets the security or performance specification, as well as genetic algorithm operations if anomalous data is available. Data is extracted from the received data samples and compared to the set of detectors to determine whether the mobile network data includes an anomaly.

公开(公告)号：US20230289605A1

公开(公告)日：2023-09-14

申请号：US17689185

申请日：2022-03-08

Applicant: Booz Allen Hamilton Inc.

Inventor： Fred Sun LU ,  Edward Simon Paster RAFF

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/084 ,  G06N3/0481

Abstract: A method, system, and computer program product for configuring a computer for data similarity determination using Bregman divergence may include storing a data set having plural data pairs with one or more data points corresponding to one or more features and generating a trained input convex neural network (ICNN) using the data set, the ICNN having one or more parameters. Training the ICNN may include extracting one or more features for each piece of data in the first data pair, generating an empirical Bregman divergence for the first data pair, and computing one or more gradients between the one or more features within the first data pair using known target distances and the computed empirical Bregman divergence.

公开(公告)号：US11615166B2

公开(公告)日：2023-03-28

申请号：US17130254

申请日：2020-12-22

Applicant: Booz Allen Hamilton Inc.

Inventor： Arash Rahnama-Moghaddam ,  Andre Tai Nguyen

IPC: G06F18/241 ,  G06N3/08 ,  G06N20/20 ,  G06N20/10 ,  G06V10/764 ,  G06V10/82 ,  G06N3/045

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.