Burlington, MA, United States
Burlington, MA, United States

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Grant
Agency: Department of Defense | Branch: Defense Advanced Research Projects Agency | Program: SBIR | Phase: Phase II | Award Amount: 1.50M | Year: 2015

Defending against state-of-the-art cyber attacks such as Advanced Persistent Threats requires rapid, automated, and accurate prioritization of cyber alerts to provide timely and comprehensive cyber-situational awareness. Context Aware INference for Advanced Persistent Threat (CAIN for APT) will address this important operational need by developing, evaluating, and transitioning our innovative case-based, context-aware reasoning capability. CAIN will ingest massive amounts of disparate cyber-sensor data from sources such as component-dependency analyses, intrusion detection outputs, system health diagnostics, activity-based alerting, and adversary threat reports. CAIN will then place these data into operational and environmental context using diverse and distributed sources, and do so in real-time. In Phase II, we will: (i) adapt and extend our existing inference and Bayesian concept learning technologies to the cyber domains; (ii) identify optimal inputs to CAIN, including vetted cyber-attack cases, sources of context, and evaluation data; (iii) design, build, and test a prototype CAIN system that is scalable and targeted towards transition; and (iv) test and evaluate CAIN on realistic data within realistic environments to illustrate the applicability, practicality and scalability of our system. The results of the Phase II program will demonstrate the feasibility and promise of the systems concept to be transitioned in Phase III.


Grant
Agency: Department of Defense | Branch: Missile Defense Agency | Program: SBIR | Phase: Phase I | Award Amount: 99.97K | Year: 2016

Combining information from disparate sensors can lead to better situational awareness and improved inference performance; unfortunately, traditional multi-sensor fusion cannot capture complex dependencies among different objects in a scene, nor can it exploit context to further boost performance. Integrating context information within a fusion architecture to reason cohesively about scenes of interest has tremendous promise for refining the decision space, thereby improving decision accuracy, robustness, and efficiency. The Deep Inference and Fusion Framework Utilizing Supporting Evidence (DIFFUSE) program will produce a mathematical framework, founded on rigorous probabilistic analysis and learning theory, which will result in accurate modeling of information across different targets in the scene and context-dependent high-level semantic representation of target labels. In Phase I we will: (1) develop a probabilistic modeling and learning approach to model multi-sensor data and context-dependent target label correlations; (2) develop inference algorithms based on fused multi-sensor data and context for multi-target classification; and (3) demonstrate the implications of the proposed algorithms under various target classification scenarios. The results of the Phase I program will demonstrate the feasibility and promise of the DIFFUSE system concept to be realized in Phase II. Approved for Public Release 16-MDA-8620 (1 April 16)


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT:Establishing timely, accurate, and comprehensive Space Situation Awareness (SSA) requires analytic methods that put into context the information drawn from multiple data sources. A vital feature of an SSA system is the capability to predict, and understand, space-based activities that might threaten our satellites. The Activity Learning and Inferencing for Space Situational Awareness (ALISSA) program will address this important operational need by developing, evaluating, and transitioning an innovative capability to learn models of normal satellite operation, and detect operationally relevant deviations from satellite normalcy, built on the synergistic combination of machine learning and multiple hypothesis reasoning methods. In the Phase I program we will: (1) develop an algorithmic approach and overall framework for the problem of improving SSA by learning normal behavior, detecting changes, and reasoning over the implications of those changes; (2) conduct a series of focused investigations and evaluations of algorithms for normalcy learning, anomaly detection, event reasoning; and (3) evaluate the implications of the proposed algorithms on the overall SSA system. The results of the Phase I program will demonstrate the feasibility and promise of the systems concept to be realized in Phase II.BENEFIT:Activity Learning and Inferencing for Space Situational Awareness (ALISSA) offers significant potential benefits to the US Government and commercial satellite applications. Learning normal behaviors, and detecting and reasoning about anomalies, are vital to ensure the physical safety of commercial and military satellites (e.g., through collision avoidance), in addition to enabling the development of accurate space situational awareness. Our work in ALISSA will ensure that further government investment in space situational awareness (SSA) is optimized by maximizing the ability to provide detection and understanding of anomalous satellite events. The current most likely paths for transition are SSA for military organizations (e.g., Joint Space Operations Center (JSpOC) Mission System (JMS), NASIC, NRO, and AFSOPS) and commercial satellite owner/operators.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 79.98K | Year: 2014

Successful intelligence analysis requires analysts to wade through massive stores of uncertain data to associate concepts, individuals, locations, and resources. Current data systems are either designed to support massive data search and retrieval, or automated analysis, but lack the flexibility to do both well. What is needed is a system that can balance between these two, to maintain and flexibly navigate association data at multiple levels of detail, while avoiding information loss that can occur when either too much or too little data is persisted, presented, or analyzed. In response, we will develop the Multi-level Associated Content Environment (MACE), an association database management and analysis system implemented as a multi-level graph. In Phase I, we will build a data model and system design, and conduct a proof-of-concept demonstration to show that MACE will scale to petabytes of data in Phase II. MACE will incorporate associations between entities, documents, and concepts at multiple levels of detail, and will persist analytic tool inferences with connections to source data. Using graph databases, we will achieve analytic and run-time performance successes where traditional databases fail. MACE will leverage existing open software in a plug-and-play architecture to provide an open, license-free solution.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 149.96K | Year: 2014

Increases in data volume and diversity place disproportionate burden on decision makers, who must manually manage datasets with tools that are insensitive to their missions, tasks, and objectives. In time-critical situations, information management is more likely to consume decision makers time and attention than facilitate their analysis. In response, Boston Fusion proposes to design and develop an adaptive workflow management system that flexibly responds to a users current tasking, information needs, and cognitive ability to interpret information. The system will learn, both actively (from user inputs) and passively (from user behaviors), what users need under different scenarios. We will: Provide a traceable suite of data preparation and exploration functions, enabling users to easily navigate their own workflows, and compose automated workflows for reuse. The system will learn from these traces how users invoke operations to manage information. Build task-centric models of decision maker information needs, to predict immediate data management needs, and to respond to shifts that trigger changes in information management needs. Characterize cognitive state and state changes of decision makers, to automatically adapt levels of automation, system guidance, and complexity in information presentation to match the users specific information needs.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 986.24K | Year: 2014

Typical search technology ignores the intended use of documentsmissing the opportunity to tailor prioritization and anticipate supplemental materials. Commercial campaigns employ semantic targetingtailoring content to a users predicted preferenceswhich we could incorporate into analytical workflows. What is needed is a sophisticated approach to incorporating the user as part of the data model, leveraging the data, behaviors and interests of the user, and prior analysts. Because analyst turnover is rampant, embedding legacy knowledge into the data model is critical. We will build on the semantic reasoning foundation we established in Phase I to provide a prototype search capability that automatically adapts search using analysts roles. We will learn how role-specific individuals select and navigate content, follow leads, and identify relevant data in complex, multi-step tasking to accomplish this vision. To minimize the training data burden, we rely on minimally-invasive approaches that discover the roles, semantic content, and semantic connections. We will leverage and extend work in probabilistic topic modeling to include the role information and complex operational tasking. We will work closely with PMW 150 to prepare for transition of STARTER to operational Naval users.


Grant
Agency: Department of Defense | Branch: Missile Defense Agency | Program: STTR | Phase: Phase I | Award Amount: 99.97K | Year: 2014

Boston Fusion, together with our teammate Syracuse University, propose a program of research and development, namely Uncertainty Characterization Using Copulas, which will lead to a parametric framework based on the statistical theory of copulas for modeling uncertainties in a centralized fusion architecture for the problem of target classification for ballistic missile defense applications. This program of research and development will produce a mathematical framework, founded on a rigorous theoretical analysis, which will result in accurate characterization of the uncertainties associated with different sensor outputs. This framework will naturally lead to: (1) a better understanding of both the performance and the limits of the underlying fusion architecture; (2) new ways for developing fusion algorithms with optimal or near-optimal performance; and (3) recommendations for sensor or feature selection under system resource constraints. In Phase I, we will develop a prototype system design based on a challenge problem to validate the feasibility of the proposed statistical modeling approach. The resulting modeling framework will not only help in characterizing the performance of the fusion architecture, but will also enable future development of new fusion algorithms with enhanced performance in Phase II. Approved for Public Release 14-MDA-7663 (8 January 14)


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 79.99K | Year: 2015

In addition to organic (i.e., military) sensors, non-organic (i.e., non-government) sensors are ubiquitous, but largely untapped. These sensors capture data that address critical information needs such as detecting emerging activities or pinpointing a targets location. What is needed is a novel sensor discovery and exploitation framework to harvest intelligence from opportunistic sensors and allow analysts to incorporate new sensor data and collaborate both with the system and other analystscorrelating new data with historical intelligence archives, persisting and sharing frame-level annotations, and refining sensor selection for changing needs. In response, we will design and develop an adaptable, plug-and-play system, SEE-DATA, to identify opportunistic sensors and extract features to characterize and exploit the data in real-time. In Phase I, we will: Develop methods to identify opportunistic sensors for information needs of users and algorithms, and update sensor selection for evolving operations. Design a system concept for opportunistic sensor identification and exploitation to provide plug-and-play, cloud-compatible tool integration for characterizing and exploiting real-time sensor data. Capture user requirements for specifying information needs, structuring analysis, and annotating data for search and collaboration. Demonstrate opportunistic sensor identification and exploitation using data from multiple sensors and sensor types to show the validity of our approach.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 493.19K | Year: 2015

Intelligence successes regularly require analysts to quickly wade through massive stores of uncertain data to associate and correlate concepts, individuals, locations, and resources. Current data systems support massive data search and retrieval, or automated analysis, but lack the flexibility to do both well. What is needed is a system that can balance between these two, to maintain and rapidly navigate association data at multiple levels of detail, while avoiding information loss that occurs when too much or too little data is considered. This support must happen at operational tempo, correlating new data streams with historical archives to detect and track threats and develop situational awareness.Accordingly, we are developing MACE, a multi-modal association database management and analysis system implemented as a multi-level graph. In Phase I, we built a system design, and conducted a proof-of-concept demonstration to show that MACE will scale to operational data volumes. In Phase II, we will extend MACE to additional modes of data, improve entity resolution, and provide recommendations to ensure robust analysis. Using graph databases, we will achieve analytic and run-time performance successes where traditional databases fail. MACE will leverage existing open software in a plug-and-play architecture to provide an open, license-free solution.


Grant
Agency: Department of Defense | Branch: Missile Defense Agency | Program: STTR | Phase: Phase II | Award Amount: 993.90K | Year: 2015

Boston Fusion, together with our teammate Syracuse University, propose a program of research and development, Uncertainty Characterization Using Copulas (UC)2, that will result in a parametric framework based on the statistical theory of copulas for modeling uncertainties for the problem of object classification. (UC)2 will produce a mathematical framework, founded on rigorous theoretical analysis, along with algorithms and software that will accurately characterize uncertainties associated with different sensor outputs. (UC)2 will naturally lead to (1) a better understanding of both the performance and the limits of the underlying fusion architecture; (2) new and enhanced fusion algorithms with optimal or near-optimal performance under realistic operating conditions; and (3) recommendations for sensor or feature selection under system resource constraints. In Phase I, we developed the initial framework and demonstrated the feasibility of (UC)2 on real data. In Phase II, we will demonstrate the performance of the proposed approach via a high-fidelity testbed environment, reflecting the operational system requirements. Approved for Public Release, 15-MDA-8303 (1 July 15)

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