Architecture Technology Corporation

Matthew P Donovan

Judson Powers

Robert A Joyce

Stephen Kurt Brueckner

Colleen Kimball

Frank N Adelstein

Dahyun Hollister

Haim Yehuda Bar

Julia A Baker

Derek P Bronner

Scott Aloisio

Paul Nicotera

Joseph Sirianni

Daniel James Tingstrom

Tyler J Mitchell

Michael Joseph Kentley

Joseph Siranni

An adaptive training system configured to implement a virtual training environment includes a control platform, which in turn includes a processor, and a non-transitory, computer readable storage medium having encoded thereon, machine instruction executable by the processor. The system further includes media devices in communication with the processor, with native sensors implemented on each of the media devices, each native sensor operating under control of a corresponding native sensor agent; and non-native sensors implemented outside the media devices, each non-native sensor operating under control of a corresponding non-native sensor agent. The machine instructions include instructions to deploy the non-native sensor agents, activate the native sensor agents, receive and process outputs from the native sensors and the non-native sensors, and adapt, in real-time, a training exercise by changing the difficulty, fidelity, and complexity of the training exercise.

Adaptive team training evaluation system and method

The methods and systems disclosed herein generally relate to automated execution and evaluation of computer network training exercises, such as in a virtual environment. A server executes a first attack action by a virtual attack machine against a virtual target machine based on a cyber-attack scenario, wherein the virtual target machine is configured to be controlled by the user computer. The server receives a user response to the first attack action, determines, using a decision tree, a first proposed attack action based on the user response, and executes an artificial intelligence model to determine a second proposed attack action based on the user response. The server selects a subsequent attack action from the first proposed attack action and the second proposed attack action and executes the subsequent attack action by the virtual attack machine against the virtual target machine.

System for cyber-attack simulation using artificial intelligence modeling

Embodiments disclosed herein describe systems and methods for assessing vulnerabilities of embedded non-IP devices. In an illustrative embodiment, a system of assessing the vulnerabilities of embedded non-IP devices may be within a portable device. The portable device may include a plurality of wired connectors for various wired communication/data transfer protocols. The portable device may include tools for analyzing the firmware binaries of the embedded non-IP devices, such as disassemblers and modules for concrete and symbolic (concolic) execution. Based upon the disassembly and the concolic execution, the portable device may identify vulnerabilities such as buffer overflows and programming flaws in the firmware binaries.

Automated cyber evaluation system

A method for improving efficiency of a training program begins with a processor monitoring and adapting execution of a training exercise of the training program. The processor determines a training program effectiveness measure including determining trainee skill improvement demonstrated during the training exercise, and monitoring and determining correctness and timeliness of trainee actions during the training exercise. The processor then determines a training program cost measure by determining a first monetary cost for the execution of the at least one training exercise, determining a second monetary cost associated with trainee man-hours for the training exercise, and generating the training program cost measure based on the first and second monetary costs. The processor then computes a ratio of the training program effectiveness measure to the training program cost measure.

System and method for improving training program efficiency

A method for detecting, identifying, and mitigating advanced persistent threats in a computer network having one or more computers includes a processor in the computer network: receiving a request to access a resource in the computer network; identifying the request as originating from an application executing on the computer network; executing an anomaly operation to determine a behavior of the application is one of anomalous and not anomalous; executing a privilege operation to determine the request is one of permanently allowed and not-permanently allowed; granting access to the resource for both a non-anomalous-behaving application and a permanently allowed request; and generating and displaying, on a graphical user interface of the computer network, and prompt for either an anomalous-behaving application or a not-permanently allowed request.

Systems and methods for establishing and managing computer network access privileges

Disclosed herein are embodiments of systems, methods, and products that execute tools to identify non-malicious faults in source codes introduced by engineers and programmers. The tools may execute a machine learning model on the source codes to perform sentiment analysis and pattern analysis on information associated with the source codes to generate annotated source code files identifying anomalies based on the sentiment analysis and the pattern analysis. One or more threat levels are then identified and ranked based on the one or more anomalies and a ranked list of the one or more threat levels is displayed on a graphical user interface of a computer.

Systems and methods for detecting non-malicious faults when processing source codes

A computer-implemented adaptive group training method a computer accessing a virtual system and initiating a group training exercise for training a trainee group comprising one or more trainees, the group training exercise including one or more challenges to the virtual system, each of the one or more challenges including a pre-defined sequence of one or more injectable events; the computer controlling subsequent execution of the group training exercise comprising injecting the injectable events; and the computer evaluating performance of the trainee group during the subsequent execution of the group training exercise, including analyzing actions taken by the trainee group in response to each of the injections, and attributing one or more of the actions taken to a trainee.

A method for improving efficiency of a training program begins with a processor monitoring and adapting execution of a training exercise of the training program. The processor determines a training program effectiveness measure including determining trainee skill improvement demonstrated during the training exercise, and monitoring and determining correctness and timeliness of trainee actions during the training exercise. The processor then determines a training program cost measure by determining a first monetary cost for the execution of the at least one training exercise, determining a second monetary cost associated with trainee manhours for the training exercise, and generating the training program cost measure based on the first and second monetary costs. The processor then computes a ratio of the training program effectiveness measure to the training program cost measure.

The methods and systems disclosed herein generally relate to automated execution and evaluation of computer network training exercises, such as in a virtual environment. A server generates a training system having a virtual attack machine and a virtual target machine where the virtual target machine is operatively controlled by a trainee computer. The server then executes a simulated cyber-attack and monitors/collects actions and responses by the trainee. The server then executes an artificial intelligence model to evaluate the trainee's action and to identify a subsequent simulated cyber-attack (e.g., a next step to the simulated cyber-attack). The server may then train the artificial intelligence model using various machine-learning techniques using the collected data during the exercise.

Artificial intelligence modeling for cyber-attack simulation protocols

Portable vulnerability identification tool for embedded non-IP devices

Permission request system and method

This disclosure provides example techniques to invoke one or more tools, with an investigative tool. The investigative tool provides a common framework that allows investigators to invoke their own trusted tools or third-party generated tools. The investigative tool described herein seamlessly and transparently invokes the tools in accordance with an investigative profile created by the investigator.

Configurable investigative tool

An example method includes monitoring execution of one or more applications on a runtime computing system that includes a plurality of processing units, receiving, from the runtime computing system during execution of the applications, monitoring information that includes at least one of function call data or application programming interface call data associated with operations performed by the plurality of processing units during execution of the applications, importing the monitoring information into a risk model, analyzing the monitoring information within the risk model to determine one or more potential vulnerabilities and one or more impacts of the one or more vulnerabilities in the runtime computing system, and outputting, for display in a graphical user interface, a graphical representation of the one or more potential vulnerabilities and the one or more impacts within the risk model.

Software assurance system for runtime environments

An example method includes storing a scenario event list that defines one or more events associated with a training exercise, and configuring, based on the events defined in the scenario event list, one or more software agents to emulate one or more cyber-attacks against a host computing system during the training exercise, which includes configuring the software agents to save a state of one or more resources of the host computing system prior to emulating the cyber-attacks and to restore the state of the resources upon conclusion of the cyber-attacks. The example method further includes deploying the software agents for execution on the host computing system during the training exercise to emulate the cyber-attacks against the host computing system using one or more operational networks.

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Trumansburg, NY, United States of America

Matthew P Donovan