Abstracts of DHS SBIR-2015.OATS Phase II Awards
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Audentia, Inc.
2757 Bordeaux Ave
La Jolla, CA 92037-2030

Proposal Information DHS SBIR-2015.OATS-15.OATS-001-0001-II - Nonlinear Laser Wave Mixing for Trace Detection of Explosives
Topic Information 15.OATS-001 - Nonlinear Laser Wave Mixing for Trace Detection of Explosives
Award/Contract Number D15PC00245

To overcome current problems, Audentia, Inc., proposes to enhance, develop and demonstrate nonlinear laser wave-mixing detectors that offer standoff detection of explosives in their native form at ambient temperature and pressure using compact rugged laser-based designs. Taking advantage of high spectral resolution available from tunable solid-state lasers (without monochromators), our prototype in Phase II is expected to offer better chemical specificity and detection sensitivity levels with low false alarm rates. Our patented laser wave-mixing methods allow efficient use of low laser power levels (e.g., 0.01 W) and we have collected zeptomole-level Phase I results using different fixed and tunable lasers from UV, visible, near IR to mid-IR (quantum cascade laser) wavelengths. We will demonstrate our Phase II prototype, using real-world samples and conditions, for real-time detection of explosives in their native forms without the use of tags/labels or time-consuming sample preparation steps. Our university partner, Distinguished Professor Bill Tong, has more than 35 years of nonlinear laser spectroscopy experience and has published 33 papers on laser wave mixing alone.

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RAM Laboratories, Inc.
591 Camino de la Reina
Suite 610
San Diego, CA 92108-3108

Proposal Information DHS SBIR-2015.OATS-15.OATS-002-0001-II - A Real-Time Application Security Analyzer
Topic Information 15.OATS-002 - A Real-Time Application Security Analyzer
Award/Contract Number D15PC00249

Software developers are faced with a variety of security challenges when developing and deploying new systems. The software may be subject to malicious insiders, external threats and supply chain threats that access systems through poor software hygiene or the presence of zero-day vulnerabilities that the vendor is not aware of. While an array of software assurance tools have been developed that audit code at the source code or static binary level, existing tools do not perform dynamic binary analysis with source code checking to assist developers, nor do they provide a drill-down into software libraries to assist supply chain management in gaining a compliance assessment for the entire software solution. To address these shortfalls, this project extends the research and development of RAM Laboratories' Real-Time Application Security Analyzer (RASAR) tool. RASAR currently detects and characterizes security vulnerabilities (including zero-day vulnerabilities) in both under development and 3rd party software through source code analysis and dynamic binary instrumentation. This project will add capabilities to the tool suite that prioritize the vulnerabilities as defined by Common Weakness Enumeration, correlate identified binary vulnerabilities with both vulnerabilities found in the Common Vulnerability Exposure database and available source code flaws, and provide a compliance dashboard that tracks and reports supply chain issues for the user. Additionally, audit results will be visualized by the user through the use of a compliance dashboard. The resulting tool will be integrated within the Software Assurance Marketplace.

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Progeny Systems Corporation
9500 Innovation Drive
Manassas, VA 20110-2210

Proposal Information DHS SBIR-2015.OATS-15.OATS-003-0001-II - Scanigo: A Social Media Analytics Toolkit
Topic Information 15.OATS-003 - Humanitarian Assistance/Disaster Relief (HA/DR) Social Media Analytics Tools
Award/Contract Number D15PC00248

Progeny developed a social media analytics toolkit called Scanigo under an ONR SBIR N121-092. Scanigo provides a methodology and computer aided approach for identifying critical information from social media for disaster responders. Scanigo implements an Ontology-Based Information Extraction (OBIE) methodology that significantly reduces the amount of information presented to the analysts, based on computed relevance, information overload is reduced by orders-of-magnitude while passing relevant information. Scanigo has been in use supporting real-world disaster since 2012 with results of less than 1% reduction of the total data to actionable information, relevant to the domain. The purpose of this Phase II is to significantly improve Scanigo's modularity, adaptability and performance. In addition, to demonstrate utility for use-cases that supports FEMA's emergency response operations. Planned improvements include: additional data sources; componentization; NoSQL database migration; knowledge extraction; ontology learning; ontology management; and cloud deployment. The primary purpose of componentization is to support various integration opportunities that already have a social media monitoring or analysis platform. For example, the OBIE pipeline that reduces social media streams in real-time based on relevance can be integrated into existing data flow architectures. The common theme of the planned improvements is scale. Componentization will allow Scanigo to scale to other integration opportunities. The ontology improvements will allow Scanigo to scale to other domains. Implementing ontology learning and management allows end users to perform their own domain customization, scaling to other domains. And finally, the performance improvements and a cloud deployment will allow Scanigo to scale to a global market.

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