Silver Bullet Solutions, Inc. Statement 2016-10-24... · Silver Bullet Solutions, Inc. Clients Current: ... OSINT, and other sensor data sources. 1.1 Level 1 Fusion: Single Object

WWW.SILVERBULLETINC.COM

Silver Bullet Solutions, Inc. (Silver Bullet) specializes in:

1. Sensor, Data, and Information Fusion. Silver Bullet has been a key contributor to the DoD sensor and information fusion community since its inception. Our specialty is data fusion based on an underlying ontology, one that is complete in representing causality and spatio-temporal mereotopology, specialization / generalization, and other real-world relationships. This work grew out of prior work on multi-hypothesis databases, knowledge-based fusion, real-time fusion databases, and Bayes Networks.

2. Architecture & Systems Engineering. Silver Bullet provides architecture and systems engineering services. Since the mid-1990’s, Silver Bullet has been a key participant in the development of architectures and architecture methodologies, frameworks, repositories, tools, and policies. Silver Bullet is the DoD technical lead for the DoD Architecture Framework (DoDAF). Silver Bullet has experience with many architecture tools and is developing translators from their native format to a DoDAF Meta Model (DM2) structured database that supports queries, analytics, and data reuse.

3. Databases and Ontologies. Silver Bullet has developed many enterprise-level databases, data warehouses, and data repositories for many clients. Silver Bullet develops data translators and mediators. Silver Bullet was part of an international defense team to develop an ontology with a formal foundation in which mathematical principles from set theory and 4-dimensional mereotopology were applied. Silver Bullet has experimented with realtime DBMS for Combat System application. Silver Bullet is currently experimenting with big data technologies for distributed data fusion threat detection.

R&D Keywords: Sensor Data Fusion, Information Fusion, Artificial Intelligence, Automated Inference, Automated Reasoning, Knowledge-based Fusion, Bayes Networks, Big Data Analytics, Semantic Inference, Cyber Fusion, Ontologies, Real-time database, Combat System, AEGIS, Ballistic Missile Defense, SSDS, Inertial Navigation Systems, Joint Information Environment

Type of company Woman Owned Small Business

Locations Washington, DC and San Diego, CA

Ownership All United States, no foreign ownership

NAICS Code(s)* 334511, 541330, 541511, 541512, 541611, 541712, 541990

Number of years in business 19

Silver Bullet Solutions, Inc.


Clients

Current:

• Department of Defense (DoD) Chief Information Officer (CIO)

• Space and Naval Warfare Systems Center Atlantic (SPAWARSYSCEN-LANT)

• Navy Program Executive Office for Integrated Warfare Systems (PEO-IWS)

• Naval Air Systems Command (NAVAIR)

• Deputy Assistant Secretary of the Navy for Research, Development, Test, and Evaluation (DASN RDT&E)

• Maryland Procurement Office

• Army Communications-Electronics Research, Development and Engineering Center (CERDEC)

Past:

• Joint Improvised Explosive Device – Defeat Organization (JIEDDO) Counter IED Operations – Intelligence Center (COIC)

• Space and Naval Warfare Systems Command (SPAWARSYSCOM) Naval Sea Systems Command (NAVSEA) Combat ID System Engineering Team

• Naval Sea Systems Command (NAVSEA) Navy Theater Wide (NTW) Program Office

• Office of Naval Research (ONR)

• Office of the Assistant Chief of Staff for Installations Management (OACSIM)

• Office of the Assistant Secretary of Defense (OASD) for Network Infrastructure and Integration (NII)

• Office of the Under Secretary of Defense (OUSD) for Acquisition, Technology, and Logistics (AT&L)

• Space and Naval Warfare Systems Center Pacific (SPAWARSYSCENT-PAC)

• US Coast Guard (USCG)

• Air Force Materiel Command, Arnold Engineering and Development Center (AEDC)

• Chief of Naval Operations (CNO), Assessments Division (N81)

• Decision Support Center (DSC) in the Office of the Assistant Secretary of Defense (OASD) for Command, Control, Communications, and Intelligence (C3I)

• Defense Information Systems Agency (DISA)

• Department of Defense (DoD) Chief Management Office (DCMO)

• Department of the Navy Chief Information Officer (DON CIO)

• Federal Aviation Administration (FAA)

• Joint Forces Command (JFCOM)

• Joint Interoperability Test Center (JITC)


1 Sensor, Data, and Information Fusion

The Joint Directors of Laboratories (JDL) defined the four levels of data fusion shown in Figure 1 (from [1]) in the early 1990’s [2]. Data fusion levels are well-established in the data fusion community and are still in-use throughout the data fusion community [3]. Although the original JDL model was for sensor inputs like radar, EW, and sonar, it has been extended to accommodate imagery, HUMINT, COMINT, MASINT, OSINT, and other sensor data sources.

1.1 Level 1 Fusion: Single Object Estimation

Object assessment typically is estimation and prediction of entity states on the basis of observation-to-track association, continuous state estimation (e.g. kinematics) and discrete state estimation (e.g. target type and ID).

1.1.1 Next Generation Fusion Architecture

This was Small Business Innovative Research (SBIR) Phase II project in support of advanced sensor and data fusion. We participated in development of Common Data Models for Navy Open Architecture / FORCEnet (air, sub, surf, C4ISR) experiments and led the development of Common Data Adapters to interface the CDM to existing and emerging aircraft, ship, submarine, and C4I systems as shown in Figure 2. These included E-2C, AEGIS Open Architecture, BYG-1, SQQ-89, WebCOP, Composeable FORCEnet, and MH-60R. The work was to inject ontology constructs into Command and Control and weapons systems in support of future advanced data fusion, in particular, “ontology-based fusion.” The concept of ontology-based fusion was discussed in papers we have presented at information and data fusion conferences. In Phase I, we experimented with running sample fusion algorithms (tracker, associator, Bayes net) against the ontology as implemented in the “realtime” DBMS. In Phase II we were the ontology and middleware leads for experiments across Navy labs: E-2C Estel and MH-60R, AEGIS Open Architecture, BYG-1, SQQ-89, WebCOP, and Composeable FORCEnet.

Figure 1. JDL Fusion Levels

Situations|Plans

Level 0Sub-Object Assessment

Measurements

Signals/Features

Objects

Situations Resources

Plans

Level 1Object Assessment

Level 2 Situation Assessment

Level 4Process Refinement (Resource Mgmt)

Signals/Features

Objects

Situations

Plans

Situations|Plans

Level 3Impact Assessment


Figure 2. Navy Open Architecture / FORCEnet Experiments with Common Data Model

1.1.2 Bayes Net Demonstrator

The invention of Bayes Nets in the 1980’s simplified probabilistic reasoning by reducing joint probabilities to just those joined by a dependency graph. An example demonstrator for an imaginary scenario is shown in Figure 3. (The simulation can be run from) For large data fusion problems though, the elicitation of the graph can still be infeasible. Ontologies representing domain understanding could inform the topology of a Bayes Net [4, 5]. On the Silver Bullet project, “Ontology-aided Fusion”, the goal was to develop a formal ontology that could be ingested by a Bayes Net toolkit that would construct the Bayes Net from the ontology and thenceforth reason over sensor data to alert operators of anomalous behavior of vessels and


aircraft in the vicinity of Naval ships. This was considered a Level 2 data fusion process. Silver Bullet developed the ontology and the prior and conditional probabilities.

Figure 3. Bayes Net Demonstrator http://www.silverbulletinc.com/ontbayes_demo.htm

1.2 Level 2 Fusion: Object and Event Association

Situation assessment can be thought of as estimation and prediction of relations among entities, to include force structure and cross force relations, communications and perceptual influences, physical context, etc.

1.2.1 Context Influences on Fusion

This research was on the use of operational context information to aid higher-level fusion, in particular, detection of anomalous activity, e.g., a Fast Inshore Attack Craft (FIAC). It had two types of employment: a-priori (procedural) use of contextual information and a-posteriori (interpretative) use as shown in the architecture below. The a-priori function consisted of trafficability algorithms that operated on the DNC database, a Multiple Model Adaptive Estimator (MMAE) that “nudged” estimates away from untrafficable areas, and an anomaly detection hypothesis tester. The a-posteriori reasoner in this project employed mereological theory, taxonomic theory, activity theory, and exculpatory activity.


Figure 4. Context-aided Fusion

1.2.2 Cyber Data Fusion

Silver Bullet is developing a cyber data fusion capability for the Army. A data fusion approach is probabilistic, not rule based. This allows operators and analysts to adjust the probability-of-false-alarm (pFA) to probability-of-detection (pD) ratio to the level that supports their operational need, e.g., for timeliness, operator workload, completeness. Our approach leverages decades of data fusion architecture and algorithms and associated probabilistic AI R&D, e.g., [6, 7, 8], 9, 10, 11, 12). It also has an architecture that started from radar target tracking evolved to imagery and all-source fusion and that fits the cyber fusion problem well. For cyber, data fusion at Level 0 extracts features from network, host, and CD&M sensor data, detects, identifies, and localizes cyber actors and events at Level 1, links actors and events at Level 2, and assesses risks at Level 3. Level 4 is the process of adjusting cyber fusion in response to new and emerging threat TTP and Courses of Action (CoA).

Operationally, cyber fusion will be distributed over nodes and interoperate something like Figure 5. On the left an individual node’s A-Box to T-Box reasoning takes place to determine if a set of sensor data is a typeInstance to T-Box object, event, behavior, TTP, campaign, and threat actor models. To the right, nodes collaborate at single-INT (outer circle) and then multi-INT levels (inner circle) to refine estimates. Where the cyber ontology (CybOnt) comes in is with the exchange of detailed and unambiguous – mathematically structured – information between the various nodes, National to and from tactical. In the sensor and data fusion world this is

Direct Evidence

Pre-engineeredusage

NeededMissionInfo

Indirect informationbearing on belief

-- “context” –

Formal General Propositions(Ontology)

A-Priori ProcessingNumeric /

Quantitative

A-Posteriori ProcessingSymbolic / Qualitative

SubjectTree

VerbTree

ObjectTree

Context Ontology and Track File

ASRASR

HVU

HVU

HVU

DV

Tug 3 –always single

Digital Nautical Chart (DNC)

Anti-Shipping Reports DB


called Distributed Data Fusion (DDF) [13, 14] and, for the distributed and diverse algorithms to produce accurate estimates, it is essential that the exchanged data be unambiguous and interoperable.

Figure 5. A View of Distributed Cyber Fusion

1.3 Level 3 Fusion: Prediction

Impact assessment is estimation and prediction of effects on situations of planned or estimated/predicted actions by the participants; to include interactions between action plans of multiple players (e.g. assessing susceptibilities and vulnerabilities to estimated/predicted threat actions given one's own planned actions).

1.3.1 Semantic Data Associator

OSINTIMINTMASINT

COMINTSIGINTCYBINT

Multi-INT Features

OSINTIMINTMASINT

COMINTSIGINTCYBINT

PMESIIFAIREST

S&TIMIDB/IPB Node

Node

NodeNode

Node

Formal Ontology in RDF/OWL

Priors & ContextPriors & ContextPriors & ContextPriors & Context

Multi-INT Features

Node

Node

NodeNode

Node

Distributed Single-INT Fusion Map/Reduce Jobs

Multi-INT Features

OSINTIMINTMASINT

COMINTSIGINTCYBINT

Distributed Multi-INT Fusion Map/Reduce

Jobs


Figure 6. Semantic Data Associator Components

Utterances and AMoIRDF/OWL Vectors

Web 1T 5-gramLemmatization &

Loading

Output Display

Utterances and AMoIRDF/OWL Data Dictionary

Gram Frequency Data Base

Utterance & AMoI RDF/OWL Lemmatization

Vectorization

Vector Term Frequency Weighting

Clustering of Utterances to

AMoI RDF/OWL

Semantic Distance FunctionLH0/LH1

Select Why

CalculateLH0 and LH1

Elements

Display and Why

Preparation

The genesis of this product is an experiment Silver Bullet conducted an experiment applying data fusion technologies to semantics, ontologies, and big-data analytics. We presented the approach and results in the Next Generation Analytics track at the Defense Security and Sensing

conference [15] and subsequent work was done under a BAA. The specific use case was detection of networks of agents and patterns of behavior. Specifically we wanted to detect elements of an activity model of terrorist attack activity – the agents, resources, networks, and behaviors.

The key innovation is a mathematically principled semantic distance algorithm. A Gram Frequency Data Base (GFDB) derived from one month of collection by Google is the knowledge base used to compute semantic mass ratios that are somewhat like the inverse of probability mass ratios. These are then used to compute likelihood ratios between utterances. The components of SDA are shown in Figure 6. A rudimentary explanation capability we developed to explain puzzling results during development is shown on the right.


2 Architecture & Systems Engineering

2.1 DoD Architecture Framework (DoDAF) and Meta Model (DM2)

Silver Bullet has been and is a key member of the DoD’s Architecture team in direct support to the DoD CIO. Silver Bullet is DoD’s lead engineer for the DoD Architecture Framework (DoDAF). Silver Bullet was the DoD ontology lead to an international defense work group to develop an ontology for coalition data exchange. For DoDAF 2, Silver Bullet led the extension of the ontology for defense domain patterns, called DoDAF Meta Model (DM2). The DM2 is the foundation for DoDAF 2’s data-oriented architecture, as illustrated by Figure 7.

2.2 Systems Engineering and Technical Assistance

A major requirement in DoDAF 2 development was bridging from architectures to systems engineering. In our SETA role for the Navy PEO responsible for the acquisiton of navigation systems we were able to put theory to practice in the development of JCIDS documents, architectures, and systems engineering specifications. An example of an integrated positioning, navigation, C4ISR, and Combat System architecture (SV-4) is shown in Figure 8. The positioning functions are on the left (e.g., INS, Speed, stellar, etc.); the middle is distribution (e.g., GPNTS); further over is navigation (e.g., Navy ECDIS, WECDIS), and the right is training. The diagram also shows some Combat System interactions. Although many of these functions and interacitons do not yet exist, they are likely future requirements. For example, coordination between ECDIS voyage planning and operational planning is a proposed ONR FNC and distribution of unclassified and classified DNC data to the Combat System and Track Server tracks to ECDIS is in the Surface Ship Architecture Description Document. We also developed custom and hybrid architecture and systems engineering artifacts including:

Relating operatoinal MOEs and KPPs to system level MOPs (hybrid CV-2 / SV-7)

Planning technology insertions for improved sensors (e.g., atom interferometers) and alternate sensors (e.g., star map matching) (SV-8/9)

Tracing from functional flows (SV-4) to system interface identification and definition (SV-1/2/6) to IRS’s and IDD’s.

Figure 7. Ontology-based Architecture and

Systems Engineering Data


Figure 8. Example Top-Level Functional Architecture

2.3 Joint Information Environment

Silver Bullet is the DoD CIO Architecture and Engineering directorate technical reviewer for many Joint Information Environment (JIE) architectures including:

Enterprise Operations Center (EOC) / JIE Management Network (JMN)

Network Normalization and Transport (NNT) Wide Area Network (WAN) and SATCOM

Unified Capabilities (UC)

Mission Partner Environment - Information System (MPE-IS)

DoD Mobility Unclassified / Classified Capability (DMUC / DMCC)

Cyber Security (CS)/Single Security Architecture (SSA)

Installation Processing Node (IPN)

Tactical Processing Node (TPN).


3 Databases and Ontologies

3.1 Formal Ontologies

Silver Bullet has been the U.S. data model and ontology lead to the international defence work group to develop a rigorous data model for coalition data exchange and integration and mathematical analysis of exchanged datasets. IDEAS’ foundation is formal, higher-order, 4-dimensional, based on four dimensionalism [16]. It is extensional (see Extension [metaphysics]), using physical existence as its criterion for identity. In practical terms, this means the ontology is well suited to managing change-over time and identifying elements with a degree of precision that is not possible using names alone. The methodology for defining the ontology is very precise about criteria for identity by grounding reasoning about whether two things are the same using something that can be accurately identified. The ontology work Silver Bullet did on this project is relevant to the ontology-aided clustering.

3.2 Combat System Experiments with In-memory DBMS Technology

In a Navy SBIR we experimented with running sample fusion algorithms (tracker, associator, Bayes net) against the ontology as implemented in the “realtime” DBMS [17:]

Kalman tracker

element - of

subset - of

intersection

union

part - of

temporal - part - of

overlap

fusion

boundary

temporal - boundary

before - after

x

y

z

t

named - by

described - by

t

x

z

t

*

Ship/Aircraft A Ship/Aircraft B

DDS IDL structured

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XML serialization

with XSD

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BM

S

GC

CS/

JC

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synchronizer

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Joncker-Volgenant-Castanon (JVC) track associator

Bayes net classifier

1] Alan N. Steinberg Christopher L. Bowman Franklin E. White, “Revisions to the JDL Data Fusion Model”, Proc. SPIE

3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (March 1999)

[2]Office of Naval Technology, “Functional Description of the Data Fusion Process”, Data Fusion Development

Strategy, Office of Naval Technology, November, (1991)

[3] David L. Hall, Martin Liggins II (Editor), James Llinas (Editor), Handbook of Multisensor Data Fusion: Theory and

Practice, Second Edition, CRC Press, (2012)

4 McDaniel, D.M., Regian, J.W., and Schaefer, G., “Ontology Based Fusion for E-2D”, in Proceedings of the National

Symposium on Sensor and Data Fusion, Military Sensing Information Analysis Center (SENSAIC), 2005

5 McDaniel, D., “Multi-Hypothesis Database for Large-Scale Data Fusion”, Proceedings of the Fifth International

Conference on Information Fusion, International Society of Information Fusion, Sunnyvale, CA, 2002

[6] (U) Office of Naval Technology, “Functional Description of the Data Fusion Process”, Data Fusion Development

Strategy, Office of Naval Technology, November, (1991)

[7] (U) Steinberg, A. N., Bowman, C. L., White, F. E., “Revisions to the JDL Data Fusion Model”,

http://www.dtic.mil/dtic/tr/fulltext/u2/a391479.pdf

[8] (U) David L. Hall, Martin Liggins II (Editor), James Llinas (Editor), Handbook of Multisensor Data Fusion: Theory

and Practice, Second Edition, CRC Press, (2012)

[9] (U) Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan, Estimation with Applications to Tracking and

Navigation: Theory Algorithms and Software, Wiley, (2001)

[10] (U) Pearl, Judea; Probabilistic Reasoning in Intelligent Systems: Patterns of Plausible Inference; 1988

[11] (U) Sindhu Raghavana, Parag Singlab and Raymond J. Mooney, “Plan Recognition Using Statistical– Relational

Models”, in Plan, Activity, and Intent Recognition: Theory and Practice, Gita Sukthankar, Christopher Geib , Hung Hai Bui, David Pynadath, Robert P. Goldman (eds.), Elsevier Science, 2014.

[12] (U) Lawrence A. Klein, Sensor and Data Fusion Concepts and Applications, SPIE Press, 1999

[13] (U) Chee Yee Chong, D. Hall, M. Liggins and J. Llinas (editors), Distributed Data Fusion for Network-Centric

Operations, CRC Press, Nov 2012

[14] (U) Pramod K. Varshney, Distributed Detection and Data Fusion, Springer, 1997

15 David McDaniel and Gregory Schaefer, “A data fusion approach to indications and warnings of terrorist

attacks”, Proc. SPIE 9122, Next-Generation Analyst II, (May 22, 2014)

16 Sider, Theodore, Four-Dimensionalism: An Ontology of Persistence and Time, Oxford University Press, (2003)

17 McDaniel, D., and Schaefer, G.,“Real-Time DBMS for Data Fusion”, Proceedings of the National Symposium on

Sensor and Data Fusion, Infrared Information Analysis Center, 2003

Documents

Silver Bullet Solutions, Inc. Statement 2016-10-24... · Silver Bullet Solutions, Inc. Clients Current: ... OSINT, and other sensor data sources. 1.1 Level 1 Fusion: Single Object