United States

The Digital Aviation Readiness Technology Engine (DARTE) Board provides a comprehensive, up-to-date picture of future F/A-18 aircraft readiness at the squadron and buno levels across the Naval Aviation Enterprise. It acts as a visual user interface in the form of Qlik dashboards for the Mission Capable (MC) status forecast powered by machine learning in ADVANA Databricks. This tool uses models created with the DARTE framework to forecast the daily MC status of squadrons and units of a TMS across the entire fleet for the next seven days. Predictions are also interrogated with explainable AI (XAI) so that users can see the leading factors that affect the predictions. The DARTE Board can display this information in multiple functional views, providing the warfighters with a tool to derive data-driven actionable insights into future readiness.

NAML 2022

Visualizing the Digital Aviation Readiness Technology Engine (DARTE) with the Advana Platform

dashboard

predictive analytics

readiness

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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Registration for this workshop has now closed.

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



poster

Detection of Anomalous Merchant Vessel Trajectories

Traditionally, data is labelled by those willing to do the work. This can lead to large amounts of available training samples, but often falls short as popular models can take tens of thousands of labelled data points to train. With active learning, the burden of labelling is minimized. The human provides targeted answers to decrease uncertainty in the model. With the human and model working together, labelling automation through prediction is enabled. 

Active Learning: Super Charge Data Labelling

AI-Enabled Naval Tactical Kill Chain

One Enterprise Platform from the Cloud to the Edge | From securing against Cyber Kill Chain Attacks to enabling Machine Learning Systems | Key objective is to illustrate how the islands of Machine Learning activities across the Navy can be aligned with the evolution of mission areas to a DevSecOps way of delivery new software capability via the industry leading Enterprise Kubernetes platform, Red Hat OpenShift Container Platform. The end result is better information for the Warfighter to understand, decide and act (i.e. close kill chains).

Closing Kill Chains by Combining MLOps and DevOps

Forecasting algorithms allow for predictions of future changes of systems. A forecasted Inventory quantity can empower leadership to preemptively adjust funded resource levels via a forecasted data demand signal. Naval Aviation Support Equipment have significant Ready for Use deficits that have a direct impact to their Naval Aviation support. Current decisions made to manage these resources are based on historical and current data reports. With predictive information that allows for proactive resource management, plans for mitigating and handling deficits can be developed before readiness is impacted. 
Current research is focused on utilization of univariate forecasting algorithms such as Auto Regressive Integrated Moving Average (ARIMA) and Exponential Smoothing. These algorithms are applied to time series values such as Ready for Use (RFU) and total on hand. The data is split into a training set consisting of between the first 70%-80% of the data to build the models. The models are then used to forecast a time series equivalent in length to the remaining 30%-20% test set, and the model forecast is compared to the test set. Algorithm performance is judged by deriving accuracy measures such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Currently, more than half of forecasts performed on the metrics of the selected pieces of support equipment have a MAPE of less than 20%, however several have had MAPE values of over 100%. In order to improve forecasting performance further analysis of the time series will be performed. For instance grouping time series with common behaviors using clustering algorithms will be used to generalize insights of forecasts. Another objective is to examine other attributes of the candidate support equipment to determine additional factors for unexpected changes in the trends of the time series and identify opportunities for optimization.
The objective of this project is to develop a forecasted inventory quantity to enable decisions makers to more proactively manage resources to improve readiness. This research is ongoing, and the investigators plan to develop several well-defined models forecasting support equipment to include the data preprocessing pipeline to facilitate downstream model adoption.

Forecasting Support Equipment Inventory

Congress recently created middle tier acquisition (MTA) programs, which provide the military services rapid prototyping and fielding pathways with new program flexibilities and an explicit schedule constraint. The services are executing multiple MTAs, resulting in a set of MTA experiments related to development, execution, and governance. There is little published information on MTA performance; we used public data to quantify planned schedules. We introduce a quantified schedule risk measure based on Monte Carlo simulations. The simulations provide insights into MTA program schedule risk and program performance relative to a statistically-based reference.

Estimating Middle Tier Acquisition schedule risk

Training AI agents within a mission relevant simulation will be crucial in providing insights and suggestions to operators and strategic planners. In order to train such agents, the AI engineers should examine how to setup an AI agent to interact with its en-vironment. Representation of the simulation environment is very important to the creation of an AI agent. The state of the world and the reward received will impact the actions the agent will take. The modeling simulation environments must have the flexibility to represent a scenario in a digestible form for the AI agent. We constructed a messaging plugin to the Joint Cognitive Operations Research Environment (JCORE), to connect a Deep Q-Network based agent to a preliminary scenario. The architec-ture used to facilitate the communication between the agent and the simulation will be presented here. The project results demonstrate the ease of integrating AI agents into military modeling environments, provided some requirements are met, illustrating a pipeline process that could be replicated for other DoD projects.

AI Training framework for a medium fidelity environment.

Machine Learning (ML), in particular Deep Learning (DL), has become an essential enabling technology for the coming fourth industrial revolution, which integrates physical, digital, and biological technologies for various innovative cyber-physical-human smart systems. Time-Series Classification (TSC) is a type of problem broadly existing in science, engineering systems, and human activities. In recent years, DL has emerged as a powerful technology for solving TSC problems with many applications, in particular for Human Activity Recognition (HAR). This research presents a DL approach to Human Activity Recognition and Identification (HARI) by detecting an entity’s “behavioral signature” utilizing the signals produced by the accelerometer and gyroscope of an iPhone 6s. The research expands on the previous study by modifying the sample length used in classification along with updating the architecture and hyperparameters for the models. The methods introduced in this research have increased the identification model accuracy from 82% to 90% using a one second sample length. While classifying the entire trial length for each participant the model reaches an accuracy of 97%.

Human Activity Recognition and Identification

This presentation will detail the collaborative effort on building an ML/AI based approach for GPS Spoof Detection between BlueRISC Inc. and Raytheon Missiles and Defense’s Maritime Position, Navigation, a and Timing (PNT) Products.  The bolt-on, ML/AI-based solution operates independently of other Assured PNT (APNT) solutions and integrates with Raytheon systems e.g., GPNTS, GHOST, etc., via the inclusion of a software module.  It is complementary and scalable to other APNT solutions, and is agnostic to sensor manufacturers, supporting various GPS/INS and PNT sensor configurations.  It has been validated utilizing Raytheon provided GPNTS tactical equivalent data sets (Live Sky and Spirent GPS simulation and WSN7 INS simulated data), against sophisticated time walk-off attacks aimed at slowly overcoming an anti-jam antenna by spoofing a single and/or multiple satellites.  The solution has been validated against GPS spoof attacks occurring in the presence of active Jamming attacks as well.  Our ML/AL solution achieves high detection rates for small drifts on the order of 15-80ns, and continues to be beneficial even down to 5ns for added Warfighter situational awareness in contested environments.  Detection processing and response achieved was on the order of several milliseconds, running on a 1.3GHz processor enabling fast and efficient response.  The nature of the solution provides detection capabilities for other, previously unseen and emerging GPS spoof attacks, as well. 
 
About the speaker: Michael Rodriguez, Embedded Security Research Scientist at BlueRISC. Michael was valedictorian in his high school class and went on to earn a BS and MS in electrical engineering and computer science from MIT. In addition to BlueRISC, he worked as a firmware engineer and researcher for various government contractors. During his career he worked in securing sensor system architecture with ML/AI and was the lead embedded engineer for the design and deployment of a sensing system for a DOE nuclear site where he was the key ML/AI architect. He co-led the development effort of the ML/AI-enabled GPS Spoof Identification solution that will be discussed. 
 
About BlueRISC: BlueRISC, Inc., started in 2002, is a cleared small business that works actively with both commercial and DoD customers.  Its focus is on researching and developing solutions in the embedded system assurance space providing detection and resilience capabilities, tools and systems, to cutting-edge attack vectors.  These include software exploitation and reverse-engineering, sensor spoofing, malicious hardware modification (e.g., insertion of hardware Trojans), and others.  Many of BlueRISC’s solutions leverage ML/AI.  The company has 30+ patents and customers in 21 countries.
About Raytheon Missiles and Defense (RMD) Maritime PNT Programs: For the past decade, Raytheon Missiles and Defense has been supporting the Navy (NAVWAR PMW/A 170) in architecting and developing an Open System Architecture (OSA), Modular System Approach (MOSA) compliant, Centralized Assured PNT Data Fusion and Distribution System to support all Maritime Surface Platforms.   RMD is the OEM of the Global Positioning System (GPS)-based Positioning, Navigation, and Timing Service (GPNTS) for Systems, Software, and Hardware; and is currently in the second year of a 10-year GPNTS Software Support Contract.

Bolt-on ML/AI GPS Spoof Detection Capability

Today our data is protected by encryption software tools and libraries such as the secure sockets layer (SSL), which of course, runs on general-purpose processors. While the computing efficiency of encryption may be less concerned than other factors in multi-tenancy cloud environments with high-end server processors, it can be a critical factor in embedded computers where power budgets tightly bound the devices or high-performance computing where both latency and multiplexing computing power is a big concern. Since the inefficiency associated with general-purpose processors is increasingly becoming an issue in the post-Moore era, we study both hardware algorithm and implementation strategy on lightweight cryptographic (LWC) accelerators that can leverage a hardware specialization approach. This paper studied the efficiency of using mixed hardware languages for implementing other hash functions as GAGE with full implementation on FPGA and ASIC technologies.


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