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NASA NTRS2015

Mini-Stamp as a Micro-Display for At-A-Glance Subsystem Information for DSN Links

Operators of the Deep Space Network (DSN) attend to numerous tasks with the overall goal of providing continuous support for the world's deep space missions. This high-stakes operations environment requires operators to understand the state of the Deep Space Network and predict what will happen next. Under the Follow-the-Sun initiative which requires remote operations of the highly complex telecommunications equipment, operators will need to remain aware of the state of the entire network rather than just their own facility, and transitioning fluidly between periods of low activity and periods of high demand. I designed a micro-display for operators to see, at a glance, the state of a Deep Space Network support including its subsystems. Using in-depth user-centered and participatory design techniques to identify information requirements, I designed what I called a Postage Stamp (NTR-49720) for individual operators to be able to maintain awareness of their own assigned supports. However, under Follow the Sun, operators must remain aware of all supports. The area occupied by the Postage Stamp must shrink to allow operators to see the state of the entire system, e.g., via a Big Board posted prominently in the operations room. Micro-displays are tools for mental model re-alignment, helping operators to keep their mental models of how the system works and behaves aligned with the changing state of the complex system. Data-driven micro-displays such as the Postage Stamp and Mini-Stamp display information about the system in a consistent way. Like a traffic light, the format of the micro-display never changes: the operator always knows where to look to find a specific piece of information. The Mini-Stamp always looks like the Mini-Stamp, and all of its data fields always lie in the same place on the micro-display. Real-time data flows through the Mini-Stamp to provide information to the operator.

Holloway, Alexandra↗

NASA NTRS2017

NASA Phase 2 DAA HITL Schedule

This schedule discusses which HiTLs will be run and the timeframe for each event. This document also discusses system architecture and data flow. The simulation environments is also briefly addressed in the document including: the ground control station and the supported simulation software.

TCAS Interoperaability↗

NASA NTRS2019

Operations of the Optical Communications Demonstration for the Orion EM-2 Mission

The GSFC implementation of an Optical Communication System to demonstrate an operational optical communication link for Orion EM-2. It will serve as a base for providing an operational optical communications capability for future Orion missions. GSFC plans to maintain a development path for the optical communication flight terminal to allow commercialization and implementation on future Orion missions. The Orion optical module part of the optical communications flight terminal and its control electronics have a common architecture with the ILLUMA-T optical terminal provided by GSFC for use on the ISS. The plan is to flow data from Orion through the Optical Communication Fight Terminal to the Optical Communication Ground Terminal and reverse. NASA's Orion spacecraft is an exploratory vehicle designed for longer-duration flights beyond the Moon. Following Orion's Exploration Mission-1 (EM-1), during which the spacecraft will travel beyond the Moon, enter a distant retrograde orbit around the Moon and return to Earth unmanned, Exploration Mission-2 (EM-2) will see a crewed spacecraft complete a slightly different flight path. First crewed test flight of the Orion spacecraft, currently targeting a June 30, 2022 launch. The mission involves: One revolution in Low Earth Orbit (LEO) parking orbit to verify basic Orion systems functionality and deploy solar arrays. A single 42-hour Highly Elliptical Orbit (HEO) intermediate checkout orbit allows characterization of the Orion vehicle system performance prior to committing to a cis-lunar flight. Trans Lunar Injection (TLI) burn using Orion Service Module (SM) main engine, which sends Orion on a lunar flyby and free return. Skip reentry at lunar return velocities to splashdown off the coast of San Diego. Total mission duration is approximately 10 days. EM-2 is the first crewed mission of the Orion Spacecraft that crew brings more video up/downloads, file transfers, and real-time chats with family back home and high-rate communications enables live HD streaming for Crew conferences, Public Affairs Office (PAO) events, and significant mission events. Also still collecting vehicle data on numerous Orion subsystems via Development Flight Instrumentation (DFI) allows large data volume returns sooner for DFI and future science payloads, as opposed to waiting for end-of-mission. O2O is a demonstration of operational utility system tested as a Developmental Test Objective (DTO) and not required to meet mission requirements/success/ Flight Test Objectives (FTO). EM-2 architecture is as close to future mission operational architecture as possible.Orion subsystems (video, DFI, etc.) expected to generate ~250 GB of data in the first 24 hours of flight. Total data generated over the mission estimated to be more than 400 GBUsing S-Band alone, Orion limited to ~ 6GB of data downlink per day. Because of this limitation, Orion is planning to limit live video downlinks on EM-1 in order to downlink high priority fileswith 1 hour/day of Optical Communication, Orion could downlink ~6x more data per day (~ 36GB/day).The optical communication system is capable of multiple data rates up to at least 80 Mbps downlink for the transfer of Orion data to Earth while Orion is operating in the lunar vicinity.

Optical Communication↗

NASA NTRS2019

Numerical Study of Lander Engine Plume Impingement on the Surface of Europa

Plume exhaust from lander engines would be of concern when landing on the surface of Europa since it could have detrimental effects on both the landing surface and powered descent vehicle. The plume could also entrain particles and redirect them up toward the landing vehicle, as well as erode and contaminate the surface where science would be conducted. In this work, a numerical methodology is developed and validated to conduct a first-order assessment of individual engine plumes of a potential Europa Lander vehicle. Computational Fluid Dynamics (CFD) is used to solve the flow field inside and immediately downstream of the nozzle, while the Direct Simulation Monte Carlo (DSMC) method is applied further downstream where the flow becomes rarefied. An interface between the two domains is defined where macroscopic flow data is passed from the CFD domain to the DSMC domain, establishing a one-way coupling. Plume pressure and velocity fields, as well as ground heating, pressure and density flux profiles, are obtained at altitudes from 25 m down to 10 m, spanning the final stages of landing. The codes and methodologies used in this study are successfully validated with simulations conducted by Morris et al. of the Apollo Lunar Module Descent Engine (LMDE) exhaust plume impinging onto the lunar surface.

Lam, Rebekah↗

NASA NTRSDate not supplied

Cloud Uniformity Measurement from NASA's 2nd Fundamental Ice Crystal Icing Test - Part 2 (Temperature and Humidity)

This paper provides a characterization of the temperature and humidity variations within icing cloud flows generated during the second Fundamental Ice Crystal Icing Physics test conducted in NASA’s Propulsion Systems Laboratory, in June of 2018. The tests were conducted at airflow velocities of 85, 135, and 185 m/s, a static pressure of 44.8 kPa, and a total temperature of 7.2 ⁰C. A custom rearward facing probe and commercial total air temperature probe were used to obtain temperature and humidity profiles of the icing cloud flow leaving the exit duct plane. Additional background humidity measurements were also obtained from a commercial total water content probe. The probes captured the changes in temperature and humidity that took place at the test section after the cloud was activated. As the main altitude airflow interacts with the icing cloud, their thermodynamic properties undergo substantial changes. Due to the particle nature of the icing cloud, the droplets and ice crystals tend to redistribute within the cloud as it propagates with the main flow. Data will be presented that shows how these factors affect the thermodynamic property distribution within the icing cloud. A discussion is given of the thermal balances in the icing cloud, between the evaporating spray droplets and the airflow, as a basis for interpreting the measured results.

Crystal Icing↗

NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗

NASA NTRS2019

Numerical study of lander engine plume impingement on the surface off Europa

Plume exhaust from lander engines would be of concern when landing on the surface of Europa since it could have detrimental effects on both the landing surface and powered descent vehicle. The plume could also entrain particles and redirect them up toward the landing vehicle, as well as erode and contaminate the surface where science would be conducted. In this work, a numerical methodology is developed and validated to conduct a first-order assessment of individual engine plumes of a potential Europa Lander vehicle. Computational Fluid Dynamics (CFD) is used to solve the flow field inside and immediately downstream of the nozzle, while the Direct Simulation Monte Carlo (DSMC) method is applied further downstream where the flow becomes rarefied. An interface between the two domains is defined where macroscopic flow data is passed from the CFD domain to the DSMC domain, establishing a one-way coupling. Plume pressure and velocity fields, as well as ground heating, pressure and density flux profiles, are obtained at altitudes from 25 m down to 10 m, spanning the final stages of landing. The codes and methodologies used in this study are successfully validated with simulations conducted by Morris et al. [1-3] of the Apollo Lunar Module Descent Engine (LMDE) exhaust plume impinging onto the lunar surface.

Hoey, William↗

NASA NTRS2020

Communications with Curiosity during solar conjunction

This study examines the methodology for operating and communicating with NASA's Curiosity Rover (MSL) during the 2019 solar conjunction. For MSL, solar conjunction occurs when the viewing angle between the Sun and Mars from Earth's perspective falls below 3 degrees, which occurs roughly every two Earth years and lasts for about two weeks. This presents a challenge for engineers operating a vehicle on Mars because the degraded signal to noise ratio disrupts data flow between Earth and the spacecraft. As a result, operators designate a command moratorium in which no commands are sent to the rover and instead design long-term plans that are uplinked weeks in advance (rather than the nominal case of daily uplinks). Coordinating communications with the rover leading up to and following conjunction requires negotiations with several orbiters, another lander, and the Deep Space Network (DSN) each with their own set of constraints. It is the Strategic Comm Planning T coordination, which acts as a baseline for the conjunction Comm team faced additional complications such as the arrival of two new spacecraft at Mars - Roscosmos ExoMars Trace Gas Orbiter (TGO).

Laubach, Sharon↗

NASA NTRS2021

Vertiport Automation Software Architecture and Requirements

The purpose of Vertiport Automation Software Architecture and Requirements was developed to mature the Vertiport Automation System (VAS) described in the High-Density Automated Vertiport ConOps, into a set of artifacts that enable the development of a VAS prototype. The scope of document was to develop: (1) A VAS Software Architecture diagram that logically organizes VAS functionality into specific software components; (2) VAS Software Trade Study identifying existing commercial products and NASA research that can be used in part or with modifications to fulfill VAS functionality. (3) A set of VAS Functional Requirements which decompose the VAS concept into software functionality, including any interfaces and data flows needed; (4) A set of VAS Test Approaches describing a methodology for verification of the VAS Functional Requirements; and (5) An application user experience (UX) design study describing the general features needed in a VAS user interface.

Advanced Air Mobility↗

NASA NTRSDate not supplied

FRET Tutorial

In this tutorial, we present the FRET tool for writing, understanding, formalizing and analyzing requirements. In practice, requirements are typically written in natural language, which is ambiguous and consequently not amenable to formal analysis. Since formal, mathematical notations are unintuitive, requirements in FRET are entered in a restricted, natural language, called FRETish with precise unambiguous meaning. This tutorial explains how requirements can be captured in FRETish and subsequently formalized in temporal logics and in the synchronous data flow language Lustre. We show, through multiple examples, how FRET assists users in understanding FRETish requirements and clarifying subtle semantic issues through English and diagrammatic explanations as well as interactive simulation. FInally, this tutorial describes how FRET can be used to perform realizability checking for identifying conflicting requirements and the connection of FRET with (1) the CoCoSim automated analysis tool for the verification of Simulink and Stateflow models, and (2) the Copilot runtime monitoring tool for the analysis of C programs.

FRET↗

NASA NTRS2022

The Psyche Planning Software Subsystem: Creating a Robust Toolset for a Discovery-class Mission

Psyche is a Discovery-class mission to the small metal-rich asteroid (16) Psyche, and is slated to launch in 2022. Psyche, like many missions, requires low-cost activity planning and sequence generation that serves as the backbone to overall uplink design. Such tools must be maintainable over long periods of operations, and powerful enough to solve complex issues that deep-space one-off missions encounter. In this paper we introduce cost-effective solutions that leverage inner- and open-source principles to meet a variety of common and novel use cases.The uplink process that was designed to meet these challenges is presented, as well as the data-flow through the high-level architecture of the planning software subsystem. The user-facing planning tools are described, particularly the Science Opportunity Analyzer, the Plan Editor, Psyche’s planning automation in the Blackbird framework, and Psyche Simulation Reports. All these applications are either new or have been substantially revamped to meet Psyche’s concept of operations. In particular, ensuring the entire toolchain can correctly process epoch-relative activities is discussed. Underlying the main applications are a common set of dependencies developed and maintained by a new cross-mission association of planning developers. In this way, Psyche can inherit well-tested functionality which saves effort and ensures its developers can focus on solving domain challenges. Quality control of the applications and libraries is ensured with a code-review and unit-test based novel ‘CM lite’ process. Collaboration with international industry and academia using the open-source modules is already occurring.The planning and scheduling software is designed to maximize operator awareness of the integrated plan at every step of the process and use common interfaces and file formats to easily transfer information. Design choices plus the team’s test-driven development process enables more expansive capabilities compared to the decentralized planning and sequence generation functions typical of Discovery-class orbiters without significant development cost increases. Benefits and drawbacks of Psyche’s approach are discussed, including comparison to other missions and tools where appropriate.

Ramanathan, Keshav↗

NASA NTRSDate not supplied

Integrated In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) connects individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. MATLAB was favorable to use as the main software integration tool due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of technologies, including but not limited to, the water processing Lunar Auger Dryer for ISRU (LADI) system and an oxygen extraction carbothermal reduction process. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing heater energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Efforts to update the MAIT tool are ongoing to accommodate and scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

Multi-Domain Routing in Delay Tolerant Networks

The goal of Delay Tolerant Networking (DTN) is to provide the missing ingredient for the ever-growing collection of communicating nodes in our solar system to become a Solar System Internet (SSI). Great strides have been made in modeling particular types of DTNs, such as schedule- or discovery-based. Now, analogously to the Internet, these smaller DTNs can be considered routing domains which must be stitched together to form the overall SSI. In this paper, we propose a framework for cross-domain routing in DTNs as well as methodologies for detecting these sub-domains. Example time-varying networks are given to demonstrate the techniques proposed. A basic component is the mathematical theory of sheaves, which unifies the underlying model of DTN routing algorithms, by giving rise to routing sheaves – these can be defined for the dynamic and scheduled networks as noted above, and can also be used to define the interfaces between these domains in order to route across them. An immediate application would be routing across discovery-based networks connected by scheduled networks. These DTN subdomains remain elusive, however, and need to become well-defined and properly sized for tractable computability. In particular, a balance must be determined between areas that are too large (i.e. large matrix computations) versus areas that are too small (i.e. “many” single-noded domains). Moreover, the connections between the domains should, at least locally, be chosen to optimize data flow and connectivity: we address this in three ways. First, tools from persistent homology are given to understand underlying structures, reminiscent of hierarchies in the Internet Protocol (IP) addressing. Second, we construct a notion of temporal graph curvature based on network geometry to analyze flows induced by dynamical processes on these networks. Finally, Schrodinger Bridges, a tool arising from statistical physics, are proposed as a method of constructing flows on time-evolving networks with desirable properties such as speed, robustness, and load sensitivity. We construct an approach to temporal hypergraphs to simultaneously model unicast, multicast, and broadcast, using the language of scheme theory, and then consider DTN network coding as a way to achieve network-level computation and organization. The paper concludes with a discussion and ideas for future work.

Alan Hylton↗

NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 60,000 cases during its parametric sweeps; these system iterations produced valuable insight into the optimal LADI geometry for minimizing energy demands, estimating carbothermal reactor and radiator mass relationships, and calculated the power dynamics of the electrolysis unit. Advanced efforts with advanced models will include examining multiple production targets to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy, and increase the MAIT software capability to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was updated and became the capital program in the Systems Engineering and Integration (SE&I) ISRU Modeling and Analysis (SIMA) project. Prior work was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 5,000 cases during its first round of parametric sweeps on the water processing architecture at multiple production targets; the system analysis produced valuable insight into the optimal LADI geometry that minimized energy demands, estimated effects to cold trap size and radiator requirements, and calculated the power dynamics of the electrolysis unit and liquid oxygen storage volume. Additional efforts are being made to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy and increase the MAIT software capability. Work is ongoing to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

In-Situ Resource Utilization Modeling of a Lunar Water Processing System

A key element of achieving a sustained surface presence, such as defined in NASA’s Artemis plan, is In-Situ Resource Utilization (ISRU). ISRU is the practice of using local resources to provide mission consumables that reduce system launch mass requirements, and regenerate resources (chiefly, water and oxygen) for propulsion and life support supporting both Lunar and Martian missions. ISRU systems require multiple complex processes, such as excavation, chemical reactors, and electrolysis subsystems that must operate in harmony to optimize the overall system process from beginning to end. The Mission Analysis and Integration Tool (MAIT) was previously developed with MATLAB in FY22 to connect individual subsystem models into a customized, flexible framework for the purpose of technology downselect, optimization, and end-to-end process planning. Beginning in FY24, MAIT was updated and became the capital program in the Systems Engineering and Integration (SE&I) ISRU Modeling and Analysis (SIMA) project. Prior work was leveraged and evolved using MATLAB/Simulink due to its ability to communicate with a vast number of other programming languages and makes up the backbone of data flow between inputs and outputs to the subsystem models. MAIT initially evaluated a suite of ISRU-related technologies, including the water processing Lunar Auger Dryer for ISRU (LADI) system with integrated upstream excavation and downstream electrolysis subsystems. With individual models consolidated, the MAIT tool generated over 5,000 cases during its first round of parametric sweeps on the water processing architecture at multiple production targets; the system analysis produced valuable insight into the optimal LADI geometry that minimized energy demands, estimated effects to cold trap size and radiator requirements, and calculated the power dynamics of the electrolysis unit and liquid oxygen storage volume. Additional efforts are being made to demonstrate the ability to scale ISRU technologies supporting the Space Technology Mission Directorate’s (STMD) commercialization strategy and increase the MAIT software capability. Work is ongoing to handle a wide array of ISRU system models beyond the Lunar environment, e.g. production of propellant for a Martian lander.

Avery Carlson↗

NASA NTRSDate not supplied

MOC Automation with GMSEC and the Generic Extendable Message Utility (GEMU)

Automation has become critical for ground systems, improving efficiency and reliability while reducing costs across mission operations. The Goddard Mission Services Evolution Center (GMSEC) software suite has played a significant role in enabling this automation, leveraging its publish/subscribe paradigm through a message bus architecture to facilitate seamless communication and data flow. Historically, the GMSEC suite, through components like Criteria Action Table (CAT) has been pivotal in automating ground system capabilities. However, as technology advances, limitations in automation with CAT have emerged, creating an opportunity to enhance ground system automation through the introduction of GEMU. This new GMSEC component brings new capabilities and addresses specific automation constraints that CAT could not overcome, allowing for more sophisticated, flexible, and efficient message processing. GEMU, at its core, is designed to accelerate the development of custom GMSEC-compliant applications. It enables users to construct automated message processing pipelines quickly, supporting both drag-and-drop web-based configuration and scripting through a simple domain-specific language. This advancement not only simplifies the process but also reduces the time needed for implementing automated solutions. This presentation will outline GEMU’s potential value in improving mission operations automation. It will highlight the benefits of transitioning from CAT to GEMU and offer insights into how GEMU can drive operational efficiencies. We will also provide an overview of the automation capabilities of GEMU and its potential impact on mission operations centers (MOCs).

GMSEC↗

DOE OSTI2016

GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\% at the time-interval level, and 0.05\% at the IP-level with 100\% true positive rates at both.

anomaly detection↗