United States

Traffic Management (TM) is a key functionality in Navy tactical network that prioritizes and shapes traffic to meet the required network Quality-of-Service (QoS). Due to the highly disruptive, dynamic, and constrained nature of tactical networks that are not present in commercial or enterprise networks, any solution tailored to tactical networks must understand the state of the network and adapt to network condition changes. In a bandwidth constrained network, it is also imperative that a solution limits or reduces overhead. Because the packets traversing the network hold a wealth of knowledge regarding the state of the network, collecting and monitoring packets allows network devices to glean valuable information regarding the state of the network, reducing the need for overhead traffic and allowing limited resources to be more efficiently used for application traffic. This project investigates using Deep Packet Inspection (DPI) in combination with Machine Learning (ML) techniques to yield timely understanding of network conditions that can be used to tune traffic management, while minimizing overhead. 
An example of using DPI to glean network condition is leveraging the application traffic’s Transmission Control Protocol (TCP) Sequence Number Acknowledgments (ACKs) to determine bi-directional link liveness that routing protocols such as Open Shortest Path First (OSPF) also detects. This approach is successful because there are well-defined protocol data in different protocols that are used to detect the same network condition, and there is redundancy when the different protocols are used simultaneously. However, the space of DPI is extremely broad, both in terms of techniques, which extend beyond leveraging well-defined protocol data such as TCP ACKs, and the sheer volume of raw data. Even in a low bandwidth environment, control traffic, data traffic, and the meta-data embedded in all traffic that is available for analysis can be at a level that is difficult to comprehend. An accomplishment of this research is the use ML algorithms to extrapolate link quality. Experiments evaluating these data using different methodologies showed that ML algorithms performed the best at extrapolating link quality given the same raw packets. Using ML to monitor network conditions for TM provides the end user with a tool for autonomous traffic optimization under changing field conditions and network resources.
The meta-data extracted through DPI holds key information about network conditions that can be utilized to dynamically react to a change in environment and traffic in additional ways beyond pre-defined static configurations. Future work includes improving the confidence level of the predicted network conditions as well as incorporating more intelligence in the data plane with the aim of ensuring that TM meets the required network QoS reducing routing protocol overhead.

NAML 2021

Deep Packet Inspection with Machine Learning Enabling Traffic Management in Constrained Environments

tactical networks

deep packet inspection

traffic engineering

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

**Watch this video to hear about the AFCEA purpose.**

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poster

The Strategic and Technical Landscape in 2021

Leading Change and Transformation at Scale in an Era of AI and Exponential Technologies

In recent years, machine learning has made strides forward in the domain of computer vision. Some teams have attempted to use existing neural networks to analyze images generated by Particle Image Velocimetry (PIV). PIV measures dynamic flow by seeding a fluid system with particles visible to a camera but too small to disrupt the flow itself. Recently some researchers have used a neural network trained in a supervised fashion on synthetic data and been able to obtain results as accurate as current state-of-the-art methods. Another group has used a similar neural network but with an unsupervised training method resulting in a model that performs better than the supervised model for less ideal input data. Machine learning’s main advantage for PIV over the current state-of-the
art is its speed. Both conventional and current machine learning methods for analyzing PIV images can struggle to accurately analyze imagery from field measurements, where resolution may be lower, or the image may be noisy. In this work, we build a robust model for analyzing noisy and low-resolution PIV data using a semi-supervised learning approach. We benchmark accuracy and computational performance against the current state of the art on real and synthetic data and explore whether such a method will perform better for noisier input and more challenging domains.

Machine Learning For Robust Particle Image Velocimetry

Commercial satellite data collection enables vessel tracking via Automatic Identification System (AIS) observations with global coverage and high revisit rates at relatively low cost. USG maritime analysts can use such data for monitoring and analysis via maritime domain awareness (MDA) tools. While the coverage and revisit rates make these commercial data relevant for addressing a wide range of maritime domain problems, the resulting large data volume and velocity hamper human analysis from fully exploiting these data. This presents an opportunity for application of machine learning (ML) analytics to automate low-level detection of activity of interest from the data streams. This would help analysts more quickly answer time-sensitive questions and focus on decision-making instead of manual data analysis.

For DARPA’s Geospatial Cloud Analytics (GCA) program, we developed flexible ML analytics for recognizing maritime behaviors from AIS data. These analytics extend our Multi-INT Analytics for Pattern Learning and Exploitation (MAPLE) family of ML capabilities. We deployed these analytics as a cloud-based service (MAPLE as a Service – MaaS) to perform automated detection of potential illegal, unreported, and unregulated (IUU) fishing activity at tactical time scales. We validated our algorithms via a quantitative performance evaluation. We demonstrated that our approach achieves comparable performance to published results for state-of-the-art fishing detection algorithms. The baseline state-of the-art approaches are fishing gear type specific (i.e., one approach for longliner, another for trawler, and a third for purse seiner gear). In contrast, MaaS uses a single approach for all of these fishing gear types. This evaluation process used an anonymized AIS data set with fishing behavior ground truth provided by Global Fishing Watch for model training and validation. We also employed commercial AIS data from two different regions (U.S. Pacific Islands & coast of Gabon) for additional performance evaluation.

Since MaaS learns behavior models from relevant examples, we can apply MaaS to other maritime behavior recognition problems (e.g., mine laying, piracy, and research vessel activity, as well as multi-vessel behavior like transshipment or aggressive vessel formations) without having to design new algorithms for each problem. The cloud-based analytics as a service approach enables rapid integration with multiple data sources and consumers. This approach makes use of open standards that enable machine-to-machine interaction for data access, analytics processing, and analytic product publication. It admits ready extension of existing MDA tools to enhance human-machine interactions. We are currently working to integrate a prototype MaaS capability for automatically detecting fishing behaviors from AIS data with the USG web-based SeaVision MDA tool.

Machine Learning Provides SeaVision with Maritime Behavior Recognition

Distinguish a lake from a flood seems easy, but when you’re looking at an aerial photograph, factors like angle, altitude, cloud cover, and context can make the task more difficult. And when you need to identify 100,000 aerial images in order to give first responders the information they need to accelerate disaster response efforts, you need to combine the speed and accuracy of machine learning (ML) with the precision of human judgement. Join to learn more about how building an end-to-end pipeline that ingests LADI and other related data using a classifier that incorporates Amazon Augmented AI can speed access to data and improve the outputs of the model for responders and the command center to extend situational awareness for planning and decision making.


Using Augmented AI and machine learning on AWS for Low Altitude Disaster Imagery (LADI)

Developing systems capable of learning at the edge post-deployment can greatly expand AI’s ability to adapt to changes in the environment and incorporate new information that may not be available prior to its deployment. Biological systems have become extremely adept at processing and learning complex signals with few computational resources in real time. In this work, we use the insect’s processing and internal representation of polarized light and auditory signals as inspiration to demonstrate archictectures capable of learning new RF signals post-deployment. Our architecture works with the complex envelope as input, and creates a robust internal representation that projects the in-phase and quadrature components into a higher dimensional space. It also relies on modulated synaptic plasticity rules to learn specific RF patterns in real time. In order to validate our architecture we have constructed a dataset of words consisting of specific sequences of multiple symbols, and subject our architectures to two types of online learning assays: in the online learning task the architecture is receiving a randomized stream of RF signals and needs to learn the vocabulary in a supervised fashion in a single experiment in real time. In the continual learning task, different patterns are shown sequentially, and the system needs to demonstrate the ability to incorporate this new information and build a larger vocabulary while minimizing catastrophic forgetting. For this presentation we will focus on vocabularies encoded using 8-PSK and 16-QAM modulation schemes. However, the proposed approach is general enough that could be applicable to any type of signal amenable to extract its complex envelope. This research has been partially funded through DARPA’s Lifelong Learning Machine program and Argonne’s Laboratory Directed Research and Development program.

Online and Continual Learning of RF Data Using Brain-Inspired Architectures

Autonomous systems, both cyber and physical, operate in highly contested environmentsin which it must be assumed that adversaries are capable, agile and informed about state-of-the-arttechnology. In order to sustain performance in such environments, systems must be able to adaptautonomously through online machine learning - that is, improve their performance quickly, evenover the duration of a single engagement. In this presentation, we outline the challenges of adapting insuch environments and propose a solution approach that leverages a variety of recent innovations inreinforcement learning, distributed computing and trusted consensus algorithms such as Blockchain. We argue that multi-component systems operating in contested environments must leverage theirredundancy for learning while also remaining resilient with respect to component failures and com-promises. In particular, to enable and accelerate learning, such systems will have to allow somenumber of components to operate sub-optimally to achieve the right exploration-exploitation bal-ance needed for rapid and effective reinforcement learning. At the same time that some number ofcomponents are possibly being sacrificed due to sub-optimal performance, the underlying missionof the system must be maintained. This leads to challenges in distributed trusted computing suchas Byzantine agreement problems. We include a discussion of the role of current Blockchain tech-nology in supporting future multiagent adaptive adversarial autonomy with the desired associatedresilience.

Resilient Distributed Adaptive Cyber-Defense Using Blockchain

Data and analytics are strategic assets supporting the Department of the Navy’s DON Information Superiority Vision (ISV). Exploiting our data drives the critical insights and decision-making that inform the full range of DON missions and functions, from integrated joint operations to the care and feeding of the Naval enterprise. This session will discuss how Sailors, Marines, and Civilians are leveraging Jupiter, the Naval Enterprise Data & Analytics environment, to integrate, share, and exploit data at speed and scale across the Warfighting, Business, and Readiness pillars. Jupiter makes DON data widely discoverable, accessible, understandable, and usable across the Naval enterprise and arms our military and civilian decision-makers with decision support analytics and context-rich visualizations, while simultaneously providing our data workforce with access to advanced data tools. This session will focus on how four Naval organizations are leveraging Jupiter’s capabilities for data analysis, visualization, and advanced analytics to drive decision advantage.

Leveraging Jupiter for Decision Advantage Across the Department of the NavyDepartment of the Navy

Safety represents a primary consideration and formidable challenge for unmanned autonomous vehicles.   DoD Standard Practice for System Safety (MIL-STD-882E, 2012), and DoD Joint Software Systems Engineering Handbook (JSSSEH, 2012) provide standard practices and guidelines that can be applied to development, testing, and evaluation of software in unmanned autonomous systems.

This presentation highlights the process for software engineering and hazard analysis applicable to autonomous systems based on MIL-STD-882 and JSSSEH.  This process is broadly defined in terms of requirements, design, implementation, testing, verification, validation and lifecycle support tasking. 

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