Publications

Refine Results

(Filters Applied) Clear All

A cybersecurity moonshot

Author:
Published in:
IEEE Secur. Priv., Vol. 19, No. 3, May-June 2021, pp. 8-16.

Summary

Cybersecurity needs radical rethinking to change its current landscape. This article charts a vision for a cybersecurity moonshot based on radical but feasible technologies that can prevent the largest classes of vulnerabilities in modern systems.
READ LESS

Summary

Cybersecurity needs radical rethinking to change its current landscape. This article charts a vision for a cybersecurity moonshot based on radical but feasible technologies that can prevent the largest classes of vulnerabilities in modern systems.

READ MORE

PATHATTACK: attacking shortest paths in complex networks

Summary

Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inferring semantic distances between concepts on the World Wide Web. An adversary with the capability to perturb the graph might make the shortest path between two nodes route traffic through advantageous portions of the graph (e.g., a toll road he owns). In this paper, we introduce the Force Path Cut problem, in which there is a specific route the adversary wants to promote by removing a minimum number of edges in the graph. We show that Force Path Cut is NP-complete, but also that it can be recast as an instance of the Weighted Set Cover problem, enabling the use of approximation algorithms. The size of the universe for the set cover problem is potentially factorial in the number of nodes. To overcome this hurdle, we propose the PATHATTACK algorithm, which via constraint generation considers only a small subset of paths|at most 5% of the number of edges in 99% of our experiments. Across a diverse set of synthetic and real networks, the linear programming formulation of Weighted Set Cover yields the optimal solution in over 98% of cases. We also demonstrate a time/cost tradeoff using two approximation algorithms and greedy baseline methods. This work provides a foundation for addressing similar problems and expands the area of adversarial graph mining beyond recent work on node classification and embedding.
READ LESS

Summary

Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inferring semantic distances between concepts on the World Wide Web. An adversary with the capability to perturb the graph might make the shortest path...

READ MORE

Health-informed policy gradients for multi-agent reinforcement learning

Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on simple particle environments that have elements of system health, risk-taking, semi-expendable agents, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.
READ LESS

Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then...

READ MORE

Principles for evaluation of AI/ML model performance and robustness, revision 1

Summary

The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to be brittle and nonrobust. In a complex and ever-changing national security environment, it is vital that the DoD establish a sound and methodical process to evaluate the performance and robustness of AI/ML models before these new capabilities are deployed to the field. Without an effective evaluation process, the DoD may deploy AI/ML models that are assumed to be effective given limited evaluation metrics but actually have poor performance and robustness on operational data. Poor evaluation practices lead to loss of trust in AI/ML systems by model operators and more frequent--often costly--design updates needed to address the evolving security environment. In contrast, an effective evaluation process can drive the design of more resilient capabilities, ag potential limitations of models before they are deployed, and build operator trust in AI/ML systems. This paper reviews the AI/ML development process, highlights common best practices for AI/ML model evaluation, and makes the following recommendations to DoD evaluators to ensure the deployment of robust AI/ML capabilities for national security needs: -Develop testing datasets with sufficient variation and number of samples to effectively measure the expected performance of the AI/ML model on future (unseen) data once deployed, -Maintain separation between data used for design and evaluation (i.e., the test data is not used to design the AI/ML model or train its parameters) in order to ensure an honest and unbiased assessment of the model's capability, -Evaluate performance given small perturbations and corruptions to data inputs to assess the smoothness of the AI/ML model and identify potential vulnerabilities, and -Evaluate performance on samples from data distributions that are shifted from the assumed distribution that was used to design the AI/ML model to assess how the model may perform on operational data that may differ from the training data. By following the recommendations for evaluation presented in this paper, the DoD can fully take advantage of the AI/ML revolution, delivering robust capabilities that maintain operational feasibility over longer periods of time, and increase warfighter confidence in AI/ML systems.
READ LESS

Summary

The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs. While there are numerous AI/ML successes in the academic and commercial sectors, many of these systems have also been shown to...

READ MORE

Mobile capabilities for micro-meteorological predictions: FY20 Homeland Protection and Air Traffic Control Technical Investment Program

Published in:
MIT Lincoln Laboratory Report TIP-146

Summary

Existing operational numerical weather forecast systems are geographically too coarse and not sufficiently accurate to adequately support future needs in applications such as Advanced Air Mobility, Unmanned Aerial Systems, and wildfire forecasting. This is especially true with respect to wind forecasts. Principal factors contributing to this are the lack of observation data within the atmospheric boundary layer and numerical forecast models that operate on low-resolution grids. This project endeavored to address both of these issues. Firstly, by development and demonstration of specially equipped fixed-wing drones to collect atmospheric data within the boundary layer, and secondly by creating a high-resolution weather research forecast model executing on the Lincoln Laboratory Supercomputing Center. Some success was achieved in the development and flight testing of the specialized drones. Significant success was achieved in the development of the high-resolution forecasting system and demonstrating the feasibility of ingesting atmospheric observations from small airborne platforms.
READ LESS

Summary

Existing operational numerical weather forecast systems are geographically too coarse and not sufficiently accurate to adequately support future needs in applications such as Advanced Air Mobility, Unmanned Aerial Systems, and wildfire forecasting. This is especially true with respect to wind forecasts. Principal factors contributing to this are the lack of...

READ MORE

Advanced Air Mobility assessment framework: FY20 Homeland Protection and Air Traffic Control Technical Investment Program

Published in:
MIT Lincoln Laboratory Report TIP-145

Summary

Advanced Air Mobility encompasses emerging aviation technologies that transport people and cargo between local, regional, or urban locations that are currently underserved by aviation and other transportation modalities. The disruptive nature of these technologies has pushed industry, academia, and governments to devote significant investments to understand their impact on airspace risk, operational procedures, and passengers. A flexible framework was designed to assess the operational viability of these technologies and the sensitivity to a variety of assumptions. This framework is used to simulate an initial AAM implementation scenario in New York City. This scenario was created by replacing a portion of NYC taxi requests with electric vertical takeoff and landing vehicles. The framework was used to assess the sensitivity of this scenario to a variety of system assumption.
READ LESS

Summary

Advanced Air Mobility encompasses emerging aviation technologies that transport people and cargo between local, regional, or urban locations that are currently underserved by aviation and other transportation modalities. The disruptive nature of these technologies has pushed industry, academia, and governments to devote significant investments to understand their impact on airspace...

READ MORE

Combating Misinformation: HLT Highlights from MIT Lincoln Laboratory

Published in:
Human Language Technology Conference (HLTCon), 16-18 March 2021.

Summary

Dr. Joseph Campbell shares several human language technologies highlights from MIT Lincoln Laboratory. These include key enabling technologies in combating misinformation to link personas, analyze content, and understand human networks. Developing operationally relevant technologies requires access to corresponding data with meaningful evaluations, as Dr. Douglas Reynolds presented in his keynote. As Dr. Danelle Shah discussed in her keynote, it’s crucial to develop these technologies to operate at deeper levels than the surface. Producing reliable information from the fusion of missing and inherently unreliable information channels is paramount. Furthermore, the dynamic misinformation environment and the coevolution of allied methods with adversarial methods represent additional challenges
READ LESS

Summary

Dr. Joseph Campbell shares several human language technologies highlights from MIT Lincoln Laboratory. These include key enabling technologies in combating misinformation to link personas, analyze content, and understand human networks. Developing operationally relevant technologies requires access to corresponding data with meaningful evaluations, as Dr. Douglas Reynolds presented in his keynote...

READ MORE

Combating Misinformation: What HLT Can (and Can't) Do When Words Don't Say What They Mean

Author:
Published in:
Human Language Technology Conference (HLTCon), 16-18 March 2021.

Summary

Misinformation, disinformation, and “fake news” have been used as a means of influence for millennia, but the proliferation of the internet and social media in the 21st century has enabled nefarious campaigns to achieve unprecedented scale, speed, precision, and effectiveness. In the past few years, there has been significant recognition of the threats posed by malign influence operations to geopolitical relations, democratic institutions and processes, public health and safety, and more. At the same time, the digitization of communication offers tremendous opportunities for human language technologies (HLT) to observe, interpret, and understand this publicly available content. The ability to infer intent and impact, however, remains much more elusive.
READ LESS

Summary

Misinformation, disinformation, and “fake news” have been used as a means of influence for millennia, but the proliferation of the internet and social media in the 21st century has enabled nefarious campaigns to achieve unprecedented scale, speed, precision, and effectiveness. In the past few years, there has been significant recognition...

READ MORE

Detecting Parkinson's disease from wrist-worn accelerometry in the U.K. Biobank

Published in:
Sensors, Vol. 21, No. 6, 2021, Art. No. 2047.

Summary

Parkinson's disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.
READ LESS

Summary

Parkinson's disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we...

READ MORE

Multilayer microhydraulic actuators with speed and force configurations

Author:
Published in:
Microsyst. Nanoeng., Vol. 7, Art. No. 22, 2021.

Summary

Electrostatic motors have traditionally required high voltage and provided low torque, leaving them with a vanishingly small portion of the motor application space. The lack of robust electrostatic motors is of particular concern in microsystems because inductive motors do not scale well to small dimensions. Often, microsystem designers have to choose from a host of imperfect actuation solutions, leading to high voltage requirements or low efficiency and thus straining the power budget of the entire system. In this work, we describe a scalable three-dimensional actuator technology that is based on the stacking of thin microhydraulic layers. This technology offers an actuation solution at 50 volts, with high force, high efficiency, fine stepping precision, layering, low abrasion, and resistance to pull-in instability. Actuator layers can also be stacked in different configurations trading off speed for force, and the actuator improves quadratically in power density when its internal dimensions are scaled-down.
READ LESS

Summary

Electrostatic motors have traditionally required high voltage and provided low torque, leaving them with a vanishingly small portion of the motor application space. The lack of robust electrostatic motors is of particular concern in microsystems because inductive motors do not scale well to small dimensions. Often, microsystem designers have to...

READ MORE