Publications

An in-depth understanding of microelectronics assurance in Department of Defense (DoD) missions is increasingly important as the DoD continues to address supply chain challenges. Many studies take a "bottom-up" approach, in which vulnerabilities are assessed in terms of general-purpose usage. This is beneficial in developing a general knowledge foundation. However, it does not offer much insight for program managers, technical leads, etc. to determine, for a specific mission and operating environment, the risks and requirements to using a microelectronic device. It is critical to develop a systematic approach that considers mission objectives, as the same component could be used in a weapon system or a surveillance system with significantly different requirements. We have been developing a Trusted and Assured Microelectronics (T&AM) Framework, which considers the entire system life cycle to produce mission-specific metrics and assessments. A radar system exemplar illustrates the approach and how the metric can be used as a Figure of Merit for quantitative analysis during development.

MIT Lincoln Laboratory hosted an invitation-only, one-day interdisciplinary workshop entitled
“Understanding Mission-Driven Resiliency” on behalf of the US Air Force, on March 18, 2019 at MIT
Lincoln Laboratory Beaver Works in Cambridge, MA. Participants began to bridge the gap between
government and industry to improve the resiliency of government systems to cyber attacks. The
workshop focused on understanding and defining resiliency from different perspectives and included
five panels devoted to discussing how different industries view and manage resiliency within their
organizations, the sources of resiliency within organizations and software-intensive systems, measuring
resiliency, and building resiliency within an organization or technology stack.

The Space Surveillance Telescope (SST) is an advanced optical sensor designed and tested by MIT Lincoln Laboratory for the Defense Advanced Research Projects Agency (DARPA), which is currently in the process of being integrated into the Space Surveillance Network. By operating the telescope in a manner normally intended for the discovery of small, artificial space objects, SST is serendipitously sensitive to the detection of very small asteroids as they traverse close to the Earth, passing rapidly through SST's search volume. This mode of operation stands in contrast to the standard approach for the search and discovery of asteroids and near-Earth objects (NEOs), in which longer revisit times restrict survey sensitivities to objects moving no faster than about 20 degrees/day. From data collected during SST's observation runs in New Mexico, we detail the discovery of 92 new candidate objects in heliocentric orbit whose absolute magnitudes range from H=26.4 to 35.9 (approximately 18-m to 25-cm in size). Some of these discoveries represent the smallest natural objects ever observed in orbit. We compare the candidate objects with bolide observations.

A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement of two key radar parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental results showing faster volume scan rates yielding greater warning performance are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In addition, lower false alarm ratios save cost by cutting down on work and personal time lost while taking shelter. The model is run on the existing contiguous United States weather radar network as well as hypothetical future configurations. Results show that the current radars provide a tornado-based benefit of ~$490M per year. The remaining benefit pool is about $260M per year that is roughly split evenly between coverage- and rapid-scanning-related gaps.

The problem of data integration has been around for decades, yet a satisfactory solution has not yet emerged. A new type of system called a polystore has surfaced to partially address the integration problem. Based on experience with our own polystore called Big-DAWG, we identify three major roadblocks to an acceptable commercial solution. We offer a new architecture inspired by these three problems that trades some generality for usability. This architecture also exploits modern hardware (i.e., high-speed networks and RDMA) to gain performance. The paper concludes with some promising experimental results.

Monitoring and Managing High Performance Computing (HPC) systems and environments generate an ever growing amount of data. Making sense of this data and generating a platform where the data can be visualized for system administrators and management to proactively identify system failures or understand the state of the system requires the platform to be as efficient and scalable as the underlying database tools used to store and analyze the data. In this paper we will show how we leverage Accumulo, d4m, and Unity to generate a 3D visualization platform to monitor and manage the Lincoln Laboratory Supercomputer systems and how we have had to retool our approach to scale with our systems.

We are on the cusp of a computational renaissance in space, and we should not bring past terrestrial missteps along. Commercial off-the-shelf (COTS) processors -- much more powerful than traditional rad-hard devices -- are increasingly used in a variety of low-altitude, short-duration CubeSat class missions. With this new-found headroom, the incessant drumbeat of "faster, cheaper, faster, cheaper" leads a familiar march towards Linux and a menagerie of existing software packages, each more bloated and challenging to secure than the last. Lincoln Laboratory has started a pilot effort to design and prototype an exemplar secure satellite processing platform, initially geared toward CubeSats but with a clear path to larger missions and future high performance rad-hard processors. The goal is to provide engineers a secure "grab-and-go" architecture that doesn't unduly hamstring aggressive build timelines yet still provides a foundation of security that can serve adopting systems well, as well as future systems derived from them. This document lays out the problem space for cybersecurity in this domain, derives design guidelines for future secure space systems, proposes an exemplar architecture that implements the guidelines, and provides a solid starting point for near-term and future satellite processing.

Analyzing large scale networks requires high performance streaming updates of graph representations of these data. Associative arrays are mathematical objects combining properties of spreadsheets, databases, matrices, and graphs, and are well-suited for representing and analyzing streaming network data. The Dynamic Distributed Dimensional Data Model (D4M) library implements associative arrays in a variety of languages (Python, Julia, and Matlab/Octave) and provides a lightweight in-memory database. Associative arrays are designed for block updates. Streaming updates to a large associative array requires a hierarchical implementation to optimize the performance of the memory hierarchy. Running 34,000 instances of a hierarchical D4M associative arrays on 1,100 server nodes on the MIT SuperCloud achieved a sustained update rate of 1,900,000,000 updates per second. This capability allows the MIT SuperCloud to analyze extremely large streaming network data sets.

Comprehensive thermophysical analyses of Near-Earth Asteroids (NEAs) provide important information about their physical properties, including visible albedo, diameter, composition, and thermal inertia. These details are integral to defining asteroid taxonomy and understanding how these objects interact with the solar system. Since infrared (IR) asteroid observations are not widely available, thermophysical modeling techniques have become valuable in simulating properties of different asteroid types. Several basic models that assume a spherical asteroid shape have been used extensively within the research community. As part of a program focused on developing a simulation of space-based IR sensors for asteroid search, the Near-Earth Asteroid Model (NEATM) developed by Harris, A. in 1998, was selected. This review provides a full derivation of the formulae behind NEATM, including the spectral flux density equation, consideration of the solar phase angle, and the geometry of the asteroid, Earth, and Sun system. It describes how to implement the model in software and explores the use of an ellipsoidal asteroid shape. It also applies the model to several asteroids observed by NASA's Near-Earth Object Wide-field Survey Explorer (NEOWISE) and compares the performance of the model to the observations.

The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the AI field is evolving so rapidly, the study scope was to look at the recent past and ongoing developments to lead to a set of findings and recommendations. It was important to begin with a short AI history and a lay-of-the-land on representative developments across the Department of Defense (DoD), intelligence communities (IC), and Homeland Security. These areas are addressed in more detail within the report. A main deliverable from the study was to formulate an end-to-end AI canonical architecture that was suitable for a range of applications. The AI canonical architecture, formulated in the study, serves as the guiding framework for all the sections in this report. Even though the study primarily focused on cyber security and information sciences, the enabling technologies are broadly applicable to many other areas. Therefore, we dedicate a full section on enabling technologies in Section 3. The discussion on enabling technologies helps the reader clarify the distinction among AI, machine learning algorithms, and specific techniques to make an end-to-end AI system viable. In order to understand what is the lay-of-the-land in AI, study participants performed a fairly wide reach within MIT LL and external to the Laboratory (government, commercial companies, defense industrial base, peers, academia, and AI centers). In addition to the study participants (shown in the next section under acknowledgements), we also assembled an internal review team (IRT). The IRT was extremely helpful in providing feedback and in helping with the formulation of the study briefings, as we transitioned from datagathering mode to the study synthesis. The format followed throughout the study was to highlight relevant content that substantiates the study findings, and identify a set of recommendations. An important finding is the significant AI investment by the so-called "big 6" commercial companies. These major commercial companies are Google, Amazon, Facebook, Microsoft, Apple, and IBM. They dominate in the AI ecosystem research and development (R&D) investments within the U.S. According to a recent McKinsey Global Institute report, cumulative R&D investment in AI amounts to about $30 billion per year. This amount is substantially higher than the R&D investment within the DoD, IC, and Homeland Security. Therefore, the DoD will need to be very strategic about investing where needed, while at the same time leveraging the technologies already developed and available from a wide range of commercial applications. As we will discuss in Section 1 as part of the AI history, MIT LL has been instrumental in developing advanced AI capabilities. For example, MIT LL has a long history in the development of human language technologies (HLT) by successfully applying machine learning algorithms to difficult problems in speech recognition, machine translation, and speech understanding. Section 4 elaborates on prior applications of these technologies, as well as newer applications in the context of multi-modalities (e.g., speech, text, images, and video). An end-to-end AI system is very well suited to enhancing the capabilities of human language analysis. Section 5 discusses AI's nascent role in cyber security. There have been cases where AI has already provided important benefits. However, much more research is needed in both the application of AI to cyber security and the associated vulnerability to the so-called adversarial AI. Adversarial AI is an area very critical to the DoD, IC, and Homeland Security, where malicious adversaries can disrupt AI systems and make them untrusted in operational environments. This report concludes with specific recommendations by formulating the way forward for Division 5 and a discussion of S&T challenges and opportunities. The S&T challenges and opportunities are centered on the key elements of the AI canonical architecture to strengthen the AI capabilities across the DoD, IC, and Homeland Security in support of national security.