|
Undersea Domain Multi-level Security Data Miner
Navy SBIR 2016.1 - Topic N161-050 NAVSEA - Mr. Dean Putnam - dean.r.putnam@navy.mil Opens: January 11, 2016 - Closes: February 17, 2016 N161-050 TITLE: Undersea Domain Multi-level Security Data Miner TECHNOLOGY AREA(S): Human Systems ACQUISITION PROGRAM: PEO IWS 5, Undersea Warfare Systems The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 5.4.c.(8) of the solicitation. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop an intuitive multi-level security data-mining algorithm to access historical trends and collected data that meets user security requirements through a unified operator interface for mission planning development and conclusions. DESCRIPTION: The Navy continues to focus on big data analysis, data mining, and information access. System operators are required to generate various reports tailored for multiple Fleet users. Creating reports requires intensive efforts in research, collection of data, generation of uniquely formatted reports, and reformatting the information to create new reports. Although much of the data is the same information, adequate preparation by mission operators is accomplished by accessing the various data sources to create mission plans. They also access the data imbedded in the system while at sea. Current mission preparation requires operators to spend numerous hours searching Secret Internet Protocol Router Network (SIPRNET) websites, various publications, using different classification levels for access. Current information is stored in different formats, as reference material, for the operators to page through; this includes hardcopy three-ringed binders, electronically on websites, and other systems. This information is not stored, indexed, or tagged in a manner that enables operators to rapidly query and search historical reports to conduct trend or data analytics or provide training opportunities for junior personnel. Systems such as submarine sonar systems do not currently permit operators to visualize historical trends and information in a consolidated, intuitive manner. A technology capable of providing a single point of access for all required data at multiple security levels (e.g., Secret and Top Secret) and that provides an intuitive user interface for generating accurate reports will improve efficiency, potentially lower costs by a factor of 7:1 through reductions in report generation, data collection, and mission planning. This will enable refocusing efforts of the work force to other tasks. The technology sought must rapidly access and parse data types across different sources at different security levels to produce a dedicated and intuitive user interface. The data that will be accessed includes acoustic, operational, intelligence, lessons learned, required reports, and environmental data. Once accessed, the newly mined information will be stored in a single data server for future access over the network (cloud) (Ref. 4). This technology must enable users across the fleet to access the data they need, in the format they define. The user interface will support all levels of security (e.g., Unclassified up to TS-SCI). The fused data can be available in interactive overlays. Examples are current and historical environmental data; current, projected, and historic Automatic Identification System (AIS) data; historic reconstructed contact tracks; and areas of uncertainty (AOU’s). The fused data can be available in information layers from operator analysis, comments, and station peculiarities associated with a geographic area. All connected systems will operate independently of the display type and resolution. Tagging of data collected for easy search will be enable correlation and classification prior to uploading it to the cloud. The technology will enable data from previous missions reconstructed files to be easily recalled and overlaid with current data to show history of deployments for contacts of interest and activity levels. It will also provide the ability to show history of these deployments. During the development, the Fleet users will coordinate and work with the company developers in a series of user interface design efforts. These user interface design efforts couple the operators with the developers ensuring a better understanding of technology capabilities and limitations for the operators and result in an optimal user interface for the fleet operators. There are four key components to be included in the concept. 1) A data management structure at all classification levels that restrict operator access to information they are allowed to view (Ref. 1, 2, 3, and 4). 2) A data-mining algorithm that can query, tag, access and sort large amounts of information across numerous data types and formats (Ref. 5 and 6). 3) An intuitive user interface enabling searching and displaying information such as historical geographic target tracks and environmental information or message traffic. 4) Ability for automatic parsing and auto completion of reports in various formats without the need to enter the same information multiple times. The resulting innovative concept will greatly reduce operator workload and improve situational awareness in areas of mission planning and report generation. The data mining algorithm will eliminate the need to manually collect various data types and makes usable data available to everyone with appropriate access privileges, reducing Life Cycle costs and increasing performance for mission preparation. Additionally, it increases mission capabilities because it will take less time to locate critical data needed to plan missions and evaluate success post-mission. The data mining algorithm will allow the computer to search for vital information while the operator is able to interpret the data provided instead of using valuable time in the search process. PHASE I: The company will develop a concept for an intuitive multi-level security data mining algorithm that meets the requirements as stated in the description section above. The company will demonstrate the feasibility of their concept in meeting Navy needs and will establish that the concept can be feasibly produced by sample testing, modeling and simulation and or analysis. The Phase I Option, if awarded would include the initial layout and capabilities description to build the data mining algorithm unit in Phase II. PHASE II: Based on the results of Phase I effort and the Phase II Statement of Work (SOW), the company will develop a prototype intuitive multi-level security data mining algorithm and conduct a series of user design sprints with fleet operators in developing the user interface for evaluation. The prototype will be evaluated to determine its capability in meeting the performance goals defined in the Phase II SOW and the current information assurance (IA) specifications for classification security. System performance would ideally be demonstrated through installation and prototype testing at a facility that already includes a range of security postures, from unclassified to TS-SCI. A multi-level security data mining algorithm prototype software will be delivered at the end of Phase II. PHASE III DUAL USE APPLICATIONS: The company will be expected to support the Navy in transitioning the intuitive multi-level security data mining algorithm during the appropriate Advanced Software Build (ASB) Advanced Capabilities Build (ACB) Advanced Processing Build (APB) software transition path referred to as the AxB process. The company will finalize the software design and algorithm prototype, according to the Phase III SOW, for AxB Step testing evaluation to determine its effectiveness in an operationally relevant environment. The company will support the Navy for test and validation in accordance with the appropriate AxB Peer review working group and the Test and Evaluation Working Group. The technology will have private sector commercial potential for any secure system such as banking and medical information requiring access and analysis of historical information, reports, and trend analysis. REFERENCES: 1. Adobe. "Enterprise Security Solutions using Adobe LiveCycle Rights Management ES."16 Apr 2015 Access. http://www.adobe.com/content/dam/Adobe/en/devnet/livecycle/pdfs/rm_security_tg.pdf 2. Grover, K, Lov. "From Schrodinger’s Equation to the Quantum Search Algorithm." 22 Sep 2001. 16 Apr 2015 Access. http://arxiv.org/pdf/quant-ph/0109116v1.pdf 3. Grover, K, Lov. "A fast quantum mechanical algorithm for database search." 29 May 1996. 16 Apr 2015 Access http://arxiv.org/pdf/quant-ph/9605043.pdf 4. Defense Information Systems Agency (DISA). "Department of Defense (DoD) Cloud Computing Security Requirements Guide (SRG)" 12 Jan 2015. Publication Date 18 June 2015 Access. http://iase.disa.mil/cloud_security/Documents/u-cloud_computing_srg_v1r1_final KEYWORDS: Data mining algorithm; multi-level computer security; AxB process; SIPRNET; Data Management Structure; Network Cloud TPOC-1: Pete Scala Phone: 202-781-3360 Email: peter.scala@navy.mil TPOC-2: Meg Stout Phone: 202-781-4233 Email: meg.stout@navy.mil Questions may also be submitted through DoD SBIR/STTR SITIS website.
|