2017 Research Symposium
LAS hosted its annual research symposium in Raleigh, NC. The symposium featured a keynote from SAS's vice president of cyber research and development, an overview of the lab's focus areas, project posters and demonstrations, and panel discussions on this year's research themes.
- Welcome – Ms. Kat Sohn, Deputy Director, LAS (6:28)
- Keynote Address – Mr. Bryan Harris, Vice President of Cyber Research and Development, SAS (15:24)
- LAS Overview & 2018 Focus Areas – Dr. Alyson Wilson, Principal Investigator (35:49)
- NC State Welcome – Dr. Randy Woodson Chancellor, NC State (1:04:00)
- Anticipatory Thinking Panel Discussion (1:09:11)
- Analytics and Analysis Panel Discussion (1:48:00)
- Interdisciplinary Approaches to Mission Challenges Panel Discussion (not recorded)
We invite you to explore this year’s research, grouped by the following themes:
- Anticipatory Thinking
- Emerging Tech
- Research Transition
- Small Conflict Economies
- Structured Analytic Tradecraft
- Visualization / Human-Machine Teams
- Workflow Optimization
Imagination support for Anticipatory Thinking
Chris Argenta, Matt Lyle, Abigail Browning
Applied Research Associates, Inc. has been working with the Anticipatory Thinking (AT) team to design and prototype a software platform that helps analysts explore many feasible futures, discover the key events/outcomes that drive them, and identify potentially surprising consequences when multiple events do not go as expected. We are developing new tradecraft and technology that help teams of analysts think divergently about scenarios while systematically managing possibilities. Our platform “Scenario Explorer” is an Imagination Support Tool that will incorporate multiple collaborative structured analytic techniques (Futures Building, Extreme States, Smart Query, and What If).
Intelligent Weighted Fuzzy Time Series Model For Financial Markets Forecasting
Ruixin Yang, Paul Jones and Nagiza F. Samatova
Financial security is critical for both national and individual level. A sound and stable financial system is prerequisite for sustainable economic growth and thus risk forecast is playing a crucial role in modern financial analysis. Even a tiny improvement in markets forecasting accuracy may have a huge impact on decision making. Thus our efforts attempt to design a new framework to improve forecast accuracy for better financial decision making.
A Comprehensive Alternative Futures Analysis Method for Intelligence Analysts
Elizabeth Tencza, Judith Johnston
Key elements from four different alternative futures analysis methods combine to create a new, structured, comprehensive method which can be easily tailored to meet specific analysis requirements, and which can be scaled up or down with respect to resources and number of participants. The new method may be useful in both determining the broad outlines of possible future contingencies and in identifying which courses of action, if any, could protect US interests.
MBA-EGR 590 Fall 2017 Decision Analytics Practicum Projects
Beverly Tyler, Kevin Wright, Abigail Browning
LAS funded me in 2016 to assist in creating an MBA course that would conduct Smart City type projects. We have been pleased with the new course and the four projects students have been involved in. The final presentations will be December 4th. All of the groups will be including an assessment of potential scenarios for their analysis using the ARA Scenario Analysis tool Chris Argenta has been working so diligently on. We would like to share our projects with the larger LAS community.
Anticipatory Thinking For Smart Cities
Christopher Kampe, Jeris Jawahar, Yannis Viniotis, Mohammad Shazad, Kathleen Vogel, Sushant Gupta
We have developed a prototype which uses Anticipatory Thinking (specifically, the Futures Wheel) as a technology for city planning. We developed our application based on interviews with 10 city planners. In essence, it provides a framework for examining/visualizing policy derived implications of hypothetical initiatives (e.g. building in areas, adopting new technologies, etc.)
A Tool to Enable Anticipatory Thinking in Smart City Planning
Jeris Alan Jawahar and Sushant Gupta, Muhammad Shahzad
Anticipatory Thinking is the process of foreseeing and preparing for future outcomes; it is a systematic method for thinking about events, actions, and consequences. Anticipatory Thinking helps identify and anticipate trends and dependencies in technological, social and policy decisions and thus discern low probability high impact events. So understanding and acquiring AT skills can in a way unlock new innovations in the IoT domain of smart cities by providing city planners a more detailed representation of a city’s behavior and needs as a result of new consequences of any project or action. We therefore use this as a motivation to build a web based visualization tool resembling a Futures Wheel that can be used as a prototype to enable Anticipatory Thinking in Smart City Planning. Over the course of our project we interviewed 10 city planners, in order to better understand the nature of their job, the tools they employed, and the recurrent, professional activities which required them to employ some degree of AT. Over the course of these interviews, we exposed planners to storyboards for our application and worked to revise it according to their suggestions.
Assessing and Developing Anticipatory Thinking Skill
James Lester, Jing Feng, Bradford Mott, James Campbell, Michael Geden, Andy Smith, Randall Spain, Adam Amos-Binks
Adaptive training and support technologies have been used to improve training and performance in a number of domains. However, limited work on adaptive training has examined anticipatory thinking, which is the deliberate, divergent exploration and analysis of relevant futures to avoid surprise. Anticipatory thinking engages the process of imagining how uncertainties impact the future, helps identify leading indicators and causal dependencies of future scenarios, and complements forecasting, which focuses on assessing the likelihood of outcomes. It is particularly important for intelligence analysis, mission planning, and strategic forecasting, wherein practitioners apply prospective sense-making, scenario planning, and other methodologies to identify possible options and their effects during decision making processes. However, there is currently no underlying cognitive theory supporting specific anticipatory thinking methodologies, no adaptive technologies to support their training, and no existing measures to assess their efficacy.
We are engaged in an ongoing effort to design adaptive technologies to support the acquisition and measurement of anticipatory thinking. As a first step toward adaptive environments that support the acquisition and application of anticipatory thinking competencies, we have developed a task to measure anticipatory thinking in which participants explore uncertainties and the impacts on the future given a particular topic. We present preliminary results from a study to examine the validity of this measure and discuss multiple factors that affect anticipatory thinking including attention, inhibitory control, need for cognition, need for closure, convergent thinking, and divergent thinking. We then introduce design principles for supporting training, application, and assessment of anticipatory thinking.
Structured Analytic Tradecraft
No description available.
Embedding Structured Analytic Tradecraft in a Cloud-Based Tool
Brent Younce, Rob, Judy Johnston
In six months, using a tradecraft engineering approach, the Analysis Engine team at the Laboratory for Analytic Sciences successfully developed a structured analytic tool for deconstructing intelligence questions and generating draft reports. Tradecraft engineering coupled with embedded analytic methods offers a promising approach to tool development in the Intelligence Community.
A Model of Argumentation for Critical Thinking on Current Events
Nancy L. Green, Michael Branon, Luke Roosje
We are currently developing a tool (AVIZE: Argument Visualization and Evaluation) that will support analysts in the following ways:
• Enable analysts to draw diagrams to visualize complex issues as a network graph of inter-related supporting and counter arguments.
• Provide a set of argument diagram building blocks, acceptable patterns of reasoning called argument schemes, tailored to analysts’ tasks and domains of interest.
Embodying task-relevant knowledge, AVIZE’s argument schemes add cognitive support for argument creation and evaluation. We manually analyzed openly available documents on current affairs and abstracted a novel set of argument schemes for constructing arguments about actors’ past or current intentions based upon their observed actions and supposed goals and/or preferences, i.e., the schemes are not limited to talking about particular countries or events. Each of the schemes has an associated set of critical questions, different ways of challenging an argument of that type. For example one of the critical questions of a scheme for inferring an actor’s plan is: Is there an alternate plausible explanation for the actor’s actions? Being made aware of the critical questions may stimulate the analyst to strengthen an argument or to construct arguments for alternative viewpoints. AVIZE enables an analyst to organize information into an argument network that shows at a glance:
• what evidence supports the premises of an argument and the reliability and likelihood of the evidence
• multiple arguments supporting the same conclusion
• arguments provided in response to critical questions, and
• counter-arguments, i.e., arguments whose conclusions conflict with each other.
Structured Analytic Tradecraft Workshop
Matthew Schmidt, Lori Wachter, Colleen Stacy, Devin Shackle
Structured Analytic Tradecraft (SAT) is a type of analytic tradecraft in which internal thought processes are externalized in a systematic and transparent manner. SAT helps analysts break down a problem into steps, apply techniques to help organize the mass amounts of data, provide transparency to the analysts’ work, support effective communication, and avoid cognitive pitfalls by identifying and assessing alternative perspectives. In August, twenty analysts from across the intelligence community (IC) were invited to participate in an immersive collaboration environment to explore SAT. Teams used software tools to apply analytic techniques to relevant intelligence problems and developed new proficiencies and approaches to thinking about analytic challenges. LAS gained valuable research data on how to develop tools to aid analysts with SAT in order to improve analytic rigor for the intelligence community. The IC gained a new community focused on incorporating Structured Analytic Tradecraft into the intelligence analysis process that endures through new collaborations.
Decomposing Analytic Workflows
Matthew Schmidt, Devin Shackle
Analytic workflows consist of a wide variety of processes developed for a wide variety of needs. As part of the Analytic Component System (ACS), we have developed a functional description framework for analytic workflows based on common types of information produced during analytic workflows. We demonstrate how this framework can be used to describe a wide variety of computational, manual, and hybrid workflows. These common descriptions can then be used to compare, analyze, and integrate a diverse set of analytic workflows and components.
How Can Analysts Find Text That the Search Engine Did Not? Lessons from Cognitive Science
Robert J Sall, Stacie Sanchez, Maria Marreo, Christopher Mayhorn, Jing Feng
A vital component of cyber-analytics is the use of search engines to compile massive sets of data. When search engines are used to compile large amounts of text-based data, users may be required to perform a unique visual search in order to scan the output and further refine the reports. The following research was done to determine if a particular visual search error, involving multiple targets presented simultaneously, appears when people are searching for words in a display. Furthermore, a follow-up was carried out to help ameliorate these cognitive deficits. Implications for this research have potential to help inform human’s interactions with search engines, across a variety of contexts, including cyber-security analytics.
Analytic Component System
Matthew Schmidt, Andrew Crerar, Devin Shackle, Munindar Singh, Samuel C., Zhen Guo
An analyst’s tradecraft enables them to identify and execute various analytic tasks whose combined effect is to produce relevant intelligence from available data. Quality tradecraft enables analysts to develop rigorous analytic workflows for a wide variety of data and needs. Increasing data sizes necessitate the use of computational tools in analytic workflows to help with the increasing scale of the available data. However, these use of computational tools can stress an analyst’s tradecraft, since the tasks performed by the tools are often functionally different than the tasks an analyst would choose to do manually.
The objective of the Analytic Component System (ACS) is to support analytic tradecraft by aligning the various manual and computational tools and techniques with a general framework for decomposing analysis. The framework developed as part of the ACS enables the representation and analysis of a wide variety of manual and computational workflows. We demonstrate that existing computational analytic tools require minimal changes to allow them to interface with this general framework. Combined, these efforts provide the analyst with a library of modular analytic components that can be naturally composed into analytic workflows.
Declarative Dataflow Framework for Building the Analytic Component System
Zhen Guo, Samuel Christie, Munindar Singh
We propose a declarative framework with ancillary models for building the Analytic Component System(ACS). The ancillary models include a data model, a resource model, and an operator model. The resource and operator model together provide a basis for optimizing the enactment of a workflow over available resources, whereas the data model mainly serves to facilitate automation by helping capture a workflow purely abstractly and refine it for a particular application based on the data model.
Integrating Analytic Tools
Matthew Schmidt, Andrew Crerar
Analysts should be able to use existing tools and techniques as components of an integrated analytic workflow in the Analytic Component System (ACS). In order to accomplish this objective, these tools must interface with the ACS using the common types of analytic constructs provided by the Analytic Component Interface (ACI). This work provides an example of how existing, independently developed tools can be easily modified to interface with the ACS, which enables the tool to integrate with a wide variety of tools and workflows automatically.
Alternative Analysis for Finding Similarity in Text
Text analysis typically occurs using keywords, word clouds, usage frequency analysis, etc. Can a larger examination of writing styles be evaluated to determine the presence of a same author, same context, same message, etc? Can plagiarism software be used
as part of a text analysis effort to determine what similarity, even similarly authored text, could be revealed in larger volumes of text data?
Using Memex’s Domain-Specific Search Capabilities and Data to Reveal Clandestine Organ Trade
Duminda Wijesekera, Bo Yu
Organ trafficking (mostly Kidney) is becoming a global problem, where poor donors are ill compensated for their organs that are sold to rich recipients, although most of the money goes to middle men. The traffickers use multiple methods including the Internet, Darknet chat rooms advertisements and social contacts to arrange for clandestine organ transplants. We show a probabilistic reasoning based risk model created to estimate risks of a potential actor becoming an actor in the organ trade using Darknet searchers, open searchers privately collected data.
Interdicting Illicit Networks: A Robust Optimization Approach
German Velasquez, Maria Mayorga
Conflict economies depend on the control over illicit goods and access to global markets to trade those goods. One way to disrupt or change conflict economies is to disrupt the illicit networks in which they rely on. To this aim, we propose to use Operations Research (OR) techniques -Network Optimization and Robust Optimization- combined with Analytics to identify the vulnerabilities of illicit networks in order to effectively disrupt them. Due to the clandestine nature of illicit networks we incorporate uncertainty in our proposed models by using Robust Optimization. In this work, we provide two robust network interdiction models where flow -the amount of illicit goods moved through the network- is considered uncertain. The first model, a static robust formulation, determines the links to interdict in the network in order to minimize flow by using a limited number of resources required for interdiction. The second model, an adaptive robust formulation, determines the links to interdict in the network and the number of resources required in order to restrict the flow to a desired level at minimum cost.
Ariana Andrews, Jody Coward, John Harkins, Peter Merrill
The overarching goal of the CY2017 LAS Trafficking Exemplar is to address the problem of combating trafficking and smuggling of human beings by developing methods that can aid in the discovery of hidden commonalities and connections within criminal networks (i.e. networks with dynamic spatio-temporal connections). The team of government, academic, and industry partners working on the Trafficking exemplar has developed analytics and research methods that support real-time decisionmaking by IC analysts and/or law enforcement seeking to counter human trafficking and smuggling. The key research threads that underpin the Trafficking Exemplar include text analytics, digital currency tradecraft, and spatio-temporal visualization.
Multi-Angled Statistical Approach to Human Trafficking Detection and Profiling
Yeng Saanchi, Marshall Wang, Saran Ahluwalia, Eric Laber, Sherrie Caltagirone
Human trafficking is a form of modern-day slavery that affects millions of people. Escort websites are a primary vehicle for selling the services of trafficking victims and thus are a rich resource for anti-trafficking operations. We use data scraped from two major escort sites to build a statistical model which predicts the probability that an advertisement is for a trafficking victim. These data are also used to build a suite of interactive data visualization and exploration tools to inform intervention strategies.
Quantitative Metrics for Mandatory Training Testing Program
Carmen A Vazquez, Joseph Aguayo
The National Security Agency’s annual training program promotes and maintains a culture of security and compliance for a variety of mission-critical issues. This program is managed by the National Cryptologic School (NCS) and the Capabilities organization. In any given year, hundreds of thousands of hours are spent on the annual training program. The LAS-NCS collaboration aimed to answer the question, can we improve the efficiency of the NSA’s mandatory training program by applying LAS analytics to the testing data? Data was evaluated under multiple regimes with results showing potential savings of close to 30,000 man-hours saved when specific questions are re-written, dropped from the tests, or addressed in more detail in the teaching curriculum. LAS automated the statistical analysis process in order to ensure that type analysis was easily repeatable.
NQUEST Results: Novel Quantitative Experimental Study on Transliteration
Richard Tait, Jon Stallings, Jared Stegall, Minson Kim
We concluded research combining the art and science of transliteration of Korean personal names to show a new, accurate methodology for determining and confirming the accuracy of the current transliteration rules.
OpenKE Technology 2017
Robert Beck, John Slankas
OpenKE Technology Framework and the research and capabilities for 2017.
WestWolf Has Left the Building: The Application of Behavioral Science to Workflow Optimization
Sam Wilgus, Mark Wilson
WESTWOLF enables mission teams and their leaders to improve work center communication and pinpoint where to adjust workflows for the greatest mission impact by applying behavioral science to workflow optimization. In 2017 we have refined implementation, creating a light touch data collection process, generating reports and visualizations, and conducting cluster analysis to provide deeper insight into team dynamics. WESTWOLF has left the building! Field tests are underway at two field sites.
Literature Discovery: WOLFHUNT
Research how academic literature can assist analysts, especially in the context of the WOLFHUNT project.
LAS as Mission-Research Collaboration Incubator: The WHEELHOUSE Mission Alignment Project
Jenny Eppard, Michele Kolb, Ryan Green, Darniet Jennings, Mark Wilson, Sam Wilgus, Joe Aguayo, Ruth Tayloe, Jody Coward
WHEELHOUSE is a collaborative project that spun out of three separate but complementary projects at LAS, NSA-G and NSA-T: the LAS WESTWOLF workflow optimization project; NSA-G’s mission/competency alignment project; and the Continual Optimization of the Analytic Process (COTAP) effort at NSA-T. LAS provided the connective tissue linking these original ideas and served as the incubator for this innovative mission-research collaboration. The resulting mission alignment effort will provide impact that greatly exceeds the potential of the three projects individually.
SCADA Event Prediction for Intrusion Detection and Response
Yen-Min Huang, Sidharth Thakur, Cameron Byrd, Manny Aparicio
To meet challenges on responding to faults and intrusions of SCADA system in realtime, the project combined two machine-learning models: SCADA system event prediction and workflow recommendation. The prototype illustrates concept of operation of predicting and identifying SCADA events and feeding the predicted events to the workflow recommender to suggest and construct response workflows based on the scenario.
SCADA Open Source Tradecraft and Technology
Robert Beck, Sandra Harrell-Cook
The open source component of the 2017 SCADA Exemplar investigated ways to leverage publicly available information to support SCADA security and vulnerability analysis and help SCADA analysts develop techniques, workflows, and analytics to use this data to support their efforts. OpenKE tradecraft and technology was leveraged in this effort and the SCADA use case also helped to further develop and refine these capabilities. In particular, the SCADA work focused on domain decomposition with mind maps, open source discovery in the SCADA domain (technology, protocol, vendors, vulnerabilities), text processing (concept and structural extraction of items like equipment, country, protocol, and vulnerabilities), and near real time retrieval and aggregation (news alerts, email source handers, and ZEPPELIN based analytics
SCADA Traffic Timing Signatures: Identifying Abnormal Network Operation
Cody Tews, Cassie Seubert, Larissa Larsen, Schweitzer Engineering Laboratories
SEL is a global leader in the design and manufacture of products and services for the protection, monitoring, control, automation, and metering of electric power systems. As a partner on the SCADA exemplar, we were uniquely positioned to use our domain expertise to construct a mock substation test bed with generation, transmission, and distribution components based on real-world equipment. We identified unique signatures in the baseline SCADA traffic and developed probabilistic models that detect deviations from nominal timing characteristics that can alert users of abnormal network operation and potential malicious action.
A System to Verify Network Behavior of Known Cryptographic Clients and Industrial Controllers
Andrew Chi, Robert Cochran, Marie Nesfield, Michael K. Reiter, Cynthia Sturton
Numerous exploits of client-server protocols and applications involve modifying clients to behave in ways that untampered clients would not, such as crafting malicious packets. We develop a system for verifying in near real-time that a cryptographic client’s message sequence is consistent with its known implementation. Moreover, we accomplish this without knowing all of the client-side inputs driving its behavior. Our toolchain for verifying a client’s messages explores multiple candidate execution paths in the client concurrently, and includes a novel approach to symbolically executing cryptographic client software (e.g., TLS) in multiple passes that defers expensive functions until their inputs can be inferred and concretized. We demonstrate client verification on OpenSSL and BoringSSL to show that, e.g., Heartbleed exploits can be detected without Heartbleed-specific filtering and within seconds of the first malicious packet. On legitimate traffic our verification keeps pace with Gmail-shaped workloads, with a median lag of 0.85s. In addition, we perform a preliminary exploration of the crossover of our behavioral verification technique to industrial control networks.
Deep Packet Inspection of SCADA networks
Mustafa Faisal, Xi Qin, Kelvin Mai, Alvaro A. Cardenas
This poster represents our work on deep-packet inspection of SCADA networks, we show how to create models of normal behavior of SCADA systems and how to show this information to operators, so they can troubleshoot connections in the system. We can then use these models to detect unusual behavior and attacks.
The security of Industrial Control Systems (ICS), which includes supervisory control and data acquisition (SCADA) systems, has been and remains a focus of cyber defense and energy professionals across government, industry, and academia. SCADA systems are highly distributed systems used to remotely monitor and control the operations in industries such as water distribution, wastewater collection, electrical power grids, and oil and gas pipelines. Recently, the vulnerability of these systems has been highlighted by cyber attacks on utilities companies. The SCADA exemplar team created tradecrafts and technologies to aid analysts protecting critical infrastructure. As SCADA systems have modernized, there is more data available to aid those tasked to architect, defend, and maintain the systems. The focus of this effort was on the exploration, development, testing, and implementation of new techniques spanning 3 types of tradecrafts: open source, structured analytic, and predictive analytic tradecrafts. The Open Source Tradecraft team worked to advance the analytic workflow by developing and automating techniques, tools, capabilities, and processes to leverage publicly available information. The Structured Analytic Tradecraft explored the application of structured analytics and how SATs might add rigor, insight, and repeatability to analysis methods. The Predictive Analytic Tradecraft team’s goal was to understand the cascading local effects for intentional (malicious or accidental) critical events in SCADA. To enable these new techniques, we adapted and develop technologies to enable these processes. Underpinning all of these efforts was research and development in data engineering, data analytics, visualizations, and reporting techniques.
Histogram-Based Anomaly Detection in SCADA Networks
Many anomaly detection systems rely on attack signatures using known patterns. Other detection systems rely on changes in traffic volume. A featured-based anomaly detection system looks at one or more traffic features to detect anomalies. This research investigated the use of a single traffic feature, namely packet inter-arrival time, to determine whether anomalous packet behavior could be detected and shows that packet inter-arrival time is a suitable feature for anomaly detection.
Cyber Threat Vulnerabilities in SCADA Systems based on Operators’ Work Behaviors
SCADA Operators’ behaviors in the workplace include those that can present vulnerabilities as significant as any technical issues. This study examined linkages between tasks, cognitive psychology, and cyber threat risk factors to understand these unintended vulnerabilities. By developing a method to use empirical research to support these linkages, the study determines potential risk mitigation strategies related to Operators’ behaviors.
Algorithms for Knowledge Graph Construction
Changsung Moon, Shiou-Tian Hsu, Mingyang Xu, Paul Jones, John Slankas, Matthew Schmidt and Nagiza F. Samatova
Current Knowledge Graph (KG) construction requires analysts to generate extensive schemas in order to effectively model any new data source. Automated KG construction approaches need to address issues such as 1) Missing entity type inferencing, 2) Interpretable entity relation extraction and 3) Coherent relation building. Our efforts attempt to address these issues to facilitate an automated pipeline.
PIGFARM: multi-query optimization for Apache Pig
Carson Cumbee, Aaron Wiechmann, Sean Lynch
PIGFARM was the LAS sponsored project for the Spring 2017 NCSU Computer Science Senior Design Class. PIGFARM researched ways to perform multi-query optimization for Apache Pig on Hadoop Clusters. Students created a process and software to merge Pig scripts together so that they could reduce the amount of processing time relative to running the scripts separately.
Media Reliability and Intelligence Community Expertise
Hector Rendon, Alyson Wilson, Jared Stegall, Sheila Bent, Sarah Tulloss, Peter Merrill
Self-communication platforms have generated a myriad of outlets and news producers. In a time when traditional news organizations are being challenged, it is relevant to explore new tools and measurements that can help researchers and the public understand whether a specific outlet disseminating news could be considered reliable or not. This study is based on the expertise from the U.S. Intelligence Community analysts and on social computing research conceptualizations to offer a statistical model that replicates the reliability measurements developed by specialists in information analysis and dissemination. The results suggest that a classification algorithm could be useful to measure news media reliability. Additionally, media organizations’ characteristics, social media data, internet traffic figures, and citations can be valuable predictors for perceptions of news reliability among intelligence analysts.
Great Expectations: A Python Framework for Bringing Data Pipelines Under Test
James Campbell, Abe Gong
Great Expectations is a python framework for bringing data pipelines and products under test. It brings discipline, confidence, and acceleration to data science and engineering teams by supporting the creation and application of automated testing suites on data instead of just code. With Great Expectations, teams can save time during data cleaning and munging, accelerate ETL and data normalization, streamline analyst-to-engineer handoffs, monitor data quality in production data pipelines and data products, simplify debugging data pipelines if (when) they break, and codify assumptions used to build models when sharing with distributed teams or other analysts.
Data Labeling and Model Refinement using Enterprise Data Stores
James Campbell, Aaron Wiechmann
While continual verification and validation and refinements to models are widely recognized as best practices in an applied machine learning context, resource, process, and technology constraints can significantly impede efforts to maintain and update models. Consequently, some machine learning systems have extremely limited ability to detect and correct for model drift, refine models based on new data such as user interaction with current model results or new data labels, and improve the presentation of model results to users. In this research, we demonstrate a generalizable workflow including a labeling service, a sampling service, and an evaluation service which operate on corporate infrastructure. Continuous model quality evaluation and relying on common enterprise services helps promote confidence in model results while ensuring that data scientists can more easily comply with the specialized security, compliance, and risk considerations are the norm for the IC.
Switch Point Detection, Graph Analysis and Machine Learning for Insider Threat Detection
Kenneth Ball, Nathan Borggren, Paul Bendich, Anastasia Deckard, John Harer
We have developed and applied methods that can support the mission of a security analyst by detecting network and user changes that may be related to insider threat activity. We examine network structure and usage statistics in synthetic insider threat paradigms and in a real email network. Switch point detection algorithms can detect abrupt changes in behavior that may be associated with insider threats: we present Bayesian approaches that can detect such changes. We also demonstrate job classification through ensemble machine learning on network usage features which may be abstracted to provide prior estimates of user behavior.
Cross Company Transfer Learning of Private Data
Amritanshu Agrawal, Tim Menzies
Predicting whether a website is phishing or not phishing is usually learnt using models generated with within-company security data. Very few companies like the idea of learning from cross-company data as they do not like to share the data to others without disclosing the data where it comes from. In this study, we morphed the actual features and trained a classifier based on few selected samples on 1 data source and predicted on other multiple sources. We achieved better performance by using SVM with RBF kernel.
Entity Resolution with Societal Impacts in Statistical Machine Learning
Rebecca C. Steorts
Very often information about social entities is scattered across multiple databases. Combining that information into one database can result in enormous benefits for analysis, resulting in richer and more reliable conclusions. In practical applications, however, analysts cannot simply link records across databases based on unique identifiers, such as social security numbers, either because they are not a part of some databases or are not available due to privacy concerns. Analysts need to use methods from statistical and computational science known as entity resolution (record linkage or de-duplication) to proceed with analysis. Entity resolution is not only a crucial task for social science and industrial applications, but is a challenging statistical and computational problem itself. In this talk, we describe the past and present challenges with entity resolution, with applications to the Syrian conflict but also official statistics, and the food and music industry. This large collaboration touches on research that is crucial to problems with societal impacts that are at the forefront of both national and international news.
A Privacy Preserving Algorithm to Release Sparse High-Dimensional Histograms
Bai Li, Vishesh Karwa, Aleksandra Slavkovi, Rebecca Steorts
Combining notions of statistical utility with algorithmic approaches to address privacy risk in the presence of big data, with differential privacy (DP) as a rigorous notion of risk, is essential for sharing useful statistical products. While DP provides strong guarantees for privacy, there are often trade-offs regarding data utility and computational scalability. We propose an (ε, δ)-DP categorical data synthesizer — Stability Based Hashed Gibbs Sampler (SBHG) — to address the very challenging problem of releasing high-dimensional sparse histograms and we illustrate its ability to overcome the limitations of current data synthesizers. We combine the Stability Based Algorithm with Gibbs sampling and feature selection which leads to improved statistical utility and reduced computational efficiency. We illustrate the behavior of SBHG on both simulated data and real data.
Cyber Behavior Modeling Applied in Intelligence Tradecraft
Sean L. Guarino
In C-MAIT, we are exploring the application of cyber adversary behavior modeling to intelligence tradecraft. In ongoing research with ONR, DARPA, and Army/RDECOM, we have designed the Cyber Modeling (CyMod) framework for wargaming and predicting adversary behaviors, and exploring proactive cyber defenses to address those behaviors. In C-MAIT, we are exploring the use of CyMod at various stages of the intelligence process, including early stage formalization of adversary tactics, wargaming to test and evaluate defensive options, and modeling to support training applications. We are currently working to integrate CyMod in the upcoming Cyber Shield event to provide user behaviors masking red team attacks.
Leveraging the Internet Side of Things; in the Internet of Things
Internet of Things (IoT) devices are varied in type, function, capabilities, etc. and their use is growing at a rapid pace. With the availability of an endless number of devices, the overall understanding is not
keeping up at the same pace. A general knowledge is needed to understand how IoT devices interact
with a digital and physical ecosystem. It would be useful to determine what information is available on
the Internet regarding an IoT device, in hopes that the information will be a part of a larger corpus of
Machine Learning Behavior from the Bitcoin Blockchain
Using a dataset of de-anonymized Bitcoin transactions, we perform deep learning and machine learning to characterize behavior of some Bitcoin participants. With a goal of distinguishing illicit from licit activity, we extract features from transactions, users, and addresses and build classifiers to gain a picture of Bitcoin economics in practice.
The Internet of Things: Low Power, Wide Area Networks
Deb Crawford, Stephanie Beard
LAS is researching and developing techniques, methods, and analytics to characterize, analyze, and make sense of the volume, variety, and value of information produced by low power, wide area IoT networks of interest. Additionally, LAS is researching advanced analytics for IoT data that can characterize, make sense of, and provide insight into IoT devices, events, and behaviors. LAS is beginning this research by focusing on the LoRaWAN protocol and expanding into SigFox, LTE-M1, and NB-IoT communications.
Cryptocurrencies and Blockchain
Crytpocurrencies, and the underlying blockchain technology many employ, have boomed in popularity in 2017. Promises of decentralization, cryptographic security, and anonymity make cryptocurrencies an attractive option for nefarious actors. Current investigative techniques employed against cryptocurrencies by financial and law enforcement analysts are highly manual, time consuming, and frequently ineffective. Further, many lack scalability to handle Big Data. To better identify illicit activities conducted with cryptocurrencies, track those illicit transactions, and identify those entities responsible, new forensic software, tools, and analytic capabilities need to be developed and deployed. In particular, capabilities must address needs for data volume, characterization, and visualization.
Unmanned Aerial Vehicles
Unmanned aerial vehicles (UAVs aka Drones) continue to explode in popularity as the consumer market expands. Technological advancements and manufacturing proficiencies have seen rapid evolution of product specifications, flight capabilities, and user interfaces; significantly lowering the consumer’s barrier to entry. Drones are becoming ubiquitous; easy to obtain, operate, and weaponize, if so desired.
Visualization / Human-Machine Teams
Weather to Trust Humans or Automation: Benevolence, Uncertainty & Emergency Management
Carl Pearson, Stacie Sanchez, Samantha Schultz, William Boettcher, Joann Keyton, Roger Mayer, Chris Mayhorn
Humans can easily find themselves in high-cost situations where they must choose between suggestions made by an automated decision aid and a conflicting human adviser. Previous research indicates that trust is an antecedent to reliance, and often influences how individuals prioritize and integrate information presented from a human or automated information source. In one experiment, participants chose the appropriate route for a military convoy, based on advice from a computer-generated map or a human intelligence adviser. This
poster reports the results of several trials involving both civilian and military samples. In a second experiment, participants join a simulated group discussion between county-level emergency managers deciding on evacuation advice and the distribution of resources in
anticipation of a hurricane’s landfall. In this study, the forecast from a computerized ensemble model is pitted against advice from these other emergency managers. This poster illuminates the design of this experiment and its pending deployment in the field.
Improving Large Display Wall Interaction Through Natural User Interfaces
Brian Clee, Christopher G. Healey, Robert St. Amant
In recent years large display walls have become increasingly popular; however, due to the size and resolution of these displays, traditional input and interaction methods have not always proven effective. In this paper, we investigate natural user interfaces (NUIs) as a way to address the interaction issues with large displays. We present a framework which provides NUI based interactions for any applica- tion running on a large display wall. Through the use of natural multi-modal input from gestures and voice commands provided by our framework, we demonstrate the capabilities and advantages of a NUI system on a large public micro-tile display wall. An experi- mental study of interaction performance and usability of our NUI system supports the use of NUIs for large display wall interaction.
Building Composeable Visualizations with RawGraphs
R. Jordan Crouser, Emma Stephenson, Zheng Mu, Zoey Sun, Kelsey Hammond
In this work, we capitalize on the opportunities afforded by current open-source technology to provide composible, on-demand data visualization. This prototype system implemented as an extension of the RawGraphs project enables the analyst to easily produce and manipulate myriad different visualizations of their data. In so doing, we acknowledge the challenges inherent in changing an established analytical pattern by enabling the analyst to compare and contrast various methods of visual encoding in order to optimize the interface to best support their workflow and analytical task.
Resolving Ambiguities in Summarized Text
LAS is experimenting with natural language processing techniques to resolve ambiguities introduced during extract-based summarization. An example of such an ambiguity would be an unclear pronoun reference resulting from the elimination of a related sentence. Through a live demonstration, attendees will summarize documents and apply conference resolution methods to create more intelligible summaries.
Automated Textual Report Generation from Email Data
Markus Eger, Colin M. Potts
Report generation from large corpuses of data has been a long-term goal of the Narrative for Sensemaking project. Email traffic represents a data-rich environment that is difficult to summarize because of a number of complications, such as sparsity, a wide range of communication groups and topics, irregular syntax/usage, etc. This work tackles those issues to generate textual reports. We analyze an email corpus to determine email topics and relationships, communication clusters and patterns, and to classify emails into communicative categories. This information is this pipelined into our report generation system that manipulates the report organization and stylization to best accommodate the analyzed data. These report generation capabilities represent a substantive step forward from our previous capabilities and show the efficacy of our approach on a new domain of interest.
John Harkins, Abhilash Arivanan
Alexis Sparko, Sean Lynch
VizKit is a data exploration platform and a testbed for visualization research. Design characteristics include a modular structure to ease integration of new visualizations, UI-enabled data binning, and the capability to upload active visualizations to a centralized and community-accessible dashboard. Use is instrumented to support a variety of experiments into visualization research topics such as (User-Task)->Visualization recommendation algorithms, effective knowledge sharing through dynamic content publication, and automated visualization generation.
Collaborative Computing Prototype
Ashley Harris, Kira Lindke, Andrew Crerar, Matt Schmidt, Devin Shackle
Traditionally, the initiative to execute a computational analytic task (such as a query or a mathematical computation) comes from the analyst. This analyst-initiated workflow can create frustration (if the analyst does not know how to define the requested task) and friction (if the analyst or computational system must wait for the other to perform their task).
This proof-of-concept demonstration provides an example of how proactive computation can enable a different, and potentially beneficial, type of exploratory analysis. The key to the approach is to have the computational system execute as many computational tasks as possible on the available data. This removes some of the burden on the analyst to determine what computation to run and frees them to instead focus on identifying useful or significant results. These results can then be chained together to define more complex computational tasks, which can then be run proactively, as well.
Visualizations for Data Exploration Tasks
Ken Thompson, Jordan Crouser, John Harkins, Joe Aguayo
Do you explore data? Do you know which visualizations will help you accomplish your data exploration tasks? We will present our research on those high level data exploration tasks of analysts, the pairings of visualizations with those tasks, and when those visualizations can be used when applying Structured Analytic Techniques to solve problems, such as those encountered in the SCADA and cyber domains.
Small Conflict Economies
Building a Better State Fragility Index: Overcoming WEIRD Biases
Arnab Chakraborty, Soumendra Lahiri, Rob Johnston, William Boettcher
Policy makers have long sought early warning of the major negative events (genocide, ethnic conflict, civil war, illicit commerce, terrorism, etc.) associated with state fragility/failure. The intelligence community has, in the past, supported efforts to develop data-driven responses to this challenge, such as the Political Instability Task Force. One well-known public effort to forecast state fragility is the The Fund for Peace’s Fragile States Index (FSI). We argue that the FSI is hampered by the so-called “WEIRD” bias– its indicators are based on a worldview that is fundamentally Western, Educated, Industrialized, Rich, and Democratic. This poster reports the results of our effort to demonstrate the limitations of the FSI and to develop a new index (free of such biases) that is dynamic, valid, and timely.
An Overview of the Small Conflict Economies Exemplar
Small conflicts can lead to regional instability, illicit trade, and failing governance. The 2017 Small Conflict Economies exemplar addressed these issues with two content focus areas (improving forecasting through machine learning and improving the understanding of illicit networks through Agent Based Modeling) and two process related goals (advancing computational social science and building multidisciplinary research teams.)
Agent-Based Modeling for Illicit Networks
Conor Artman, Zhen Li, Eric Laber
When practical constraints make data collection impossible or unreliable, researchers build agent-based model simulations (ABMS) to generate artificial data and improve intuition for their problems–this makes ABMS particularly relevant for illicit network behavior. ABMS build imitations of intelligent units called “agents” in a rule-based environment to generate artificial data and to inform intuitions for hard-to-observe phenomena. Currently, there are no general standards for assessing ABMS’ performance and no general platform for comparing ABMS–this means results and data from ABMS can be unreliable. We present prospective work on building a platform for standardized ABMS, methods for analyzing ABMS sensitivity, and a specific application to illicit networks.
Enhancing Reproducibility and Reliability in Agent-Based Modeling
Conor Artman, Eric Laber
Agent-based models are a platform for simulating complex systems with many interacting and partially autonomous entities. These models are appealing when collecting high-quality observational or randomized data is not feasible; e.g., studying illegal trafficking or conflict economies. At present, however, no unified platform exists for conducting simulations using large agent-based models. Consequently, agent-based models suffer from a lack of transparency, reproducibility, and standardized performance metrics, which hinder progress in research. We propose a plug-and-play framework for agent-based modeling wherein researchers can develop, test, and benchmark their methodology in a way that is completely transparent, reproducible, and encapsulated from a complex computing backend.
Interactive Geovisualizations of Conflict Economies
Reza Amindarbari, Makiko Shukunobe, Laura Tateosian
Conflict economies, such as human trafficking, are sustained by interactions amongst the actors in these markets. Innovative visualization and data mining approaches are needed for understanding these complex, geospatially distributed economies. We investigated the use of geovisualization tools for spatial and temporal interpersonal interactions in a conflict economy. Here we present three interactive geovisualization tools we developed based on our explorations. The platforms (proprietary/open-source) and data formats (stand-alone/database) vary across the three tools. All tools link to optimization models and one also embeds temporal analytics. To test the tools, we created geospatial digraphs of interactions between actors within a potential human trafficking market. We discuss spatial and temporal patterns revealed by the visualizations.
Better Modeling of State Instability Using Correlated Variables
Luis R. Esteves, Dawn Hendricks, Samantha Schultz, Robert Beck
The ability to anticipate critical state instability before a state actually fails is a key intelligence requirement. The standard for Fragile State Indices (FSI) is flawed in the ways it accounts for the various variables that go into measuring state stability and the FSI’s ability to detect changes in state stability is limited by the periodicity of the underpinning data. Using LAS’s OpenKE software and research into correlative variables, this project seeks to create an array of dependent, independent and proxy variables that can be observed in real time to create a more timely, accurate model of state stability.
Gender in the Jihad: Characteristics of Male and Female Terrorists
Christine Shahan Brugh, Sarah L. Desmarais, Samantha Allen Zottola, Joseph M. Simons-Rudolph
Radicalization theories and risk assessment tools were developed on men, but research on far-right extremist groups suggests gender differences. Thus, there is a need for gender-informed counterterrorism efforts. Present Study Using the Western Jihadism Project (WJP), we sought to describe characteristics of female terrorists and identify gender differences in the characteristics of terrorists. Research Questions What are the characteristics of the women in the WJP? Do men and women in the WJP differ on demographics, criminality, radicalization, and foreign fighting
You Are When You Tweet: Automatic Segmentation of Consumers Based On Social Media Activity
Anthony Weishampel, William Rand
Social Media provides a far-reaching platform for social and political inﬂuencers to spread their beliefs. Firms and organizations often cannot and sometimes should not respond to nor engage with every user. Knowing as much as possible about the users is vital in order to maximize the organization’s resources. The user may clearly provide the necessary information via the social media platform, but more often than not social media profiles are empty or filled with irrelevant content. In this study, we examine the ability to automatically classify three marketing-relevant characteristics of social media users: geography, customer lifetime value, and future word of mouth. We use a machine learning method known as Causal State Modeling. Casual State Models (CSM) are able to describe and predict a user’s social media behavior. Through modeling the behaviors of users with the known desired and undesired characteristics, we are able to construct a classifier that can examine an unknown individual and classify their characteristics. CSMs are built on the individual and group levels to determine whether it is necessary to model the individual completely, or whether a simpler group-level model is sufficient for classification. We show that we are able to successfully predict some characteristics, and that the individual models performed better than the group levels for classifying the unknown users. We also describe how our general framework can include additional features that will help improve the results of our current classifier.
Radicalizer’s Cognitive-Behavioral Emotive Radicalization
William Agosto-Padilla, Joseph Aguayo, Carlos Gaztambide, Mariza Marrero
Radicalization is the transformation of an individual’s belief system to one of extreme. Although there is a significant research on the subject of Radicalization, very little attention has been given to multi-modal approaches to understand radicalizer and the influence of their messages. Our intent is to present a novel Multi-modal Radicalization Analytic paradigm which implements computer algorithms and applies cognitive behavioral-emotive theories to improve understanding of how radicalizers operate. Our Cognitive Behavioral-Emotive Radicalization (CBE-R) Analytic process will assist the Intelligence Analyst on identifying possible radicalization targets for further scrutiny. CBE-R will close a gap that currently exist in the radicalization research, and will provide a framework to better understand the cognitive, emotive, behavioral, and psychological nature of the radicalization process. Our multi-modal Analytics approach is based on melding novel Computer Science Programming construct of Sentiment and Affect analysis with Machine Learning/Deep Learning algorithms and Cognitive Behavioral-Emotive Theories.
Characterizing How Developers Join GitHub Organizations By Their Project Contributions
Justin A. Middleton
Open-source software often depends on volunteer contributions for maintenance, so development teams must foster communities of part-time contributors to take on development work. Modern source code management websites offer many ways for contributors to interact with open-source projects, and some contributors continue their work to eventually become recognized members of the project’s development team with more freedom and influence to act upon the project’s direction. In this work, we examine which forms of software contributions most clearly characterize part-time contributors who eventually join development organizations against those who remain organization outsiders. We analyze thousands of GitHub interactions between individual developers and organizations and compare projects roles from two snapshots in time to discern which forms of contributions correlate most with a given user’s movement into the group between snapshots. We find that increased activity in general correlates with an increased rate of joining for most forms of contributions, yet some specific contributions have a negative impact. Furthermore, we also find evidence that the social activity of GitHub contributors might be just as important as technical contributions.
Developing Radicalization Analysis Tradecraft
Mariza Marrero, Sarah Margaret Tulloss, Felecia Vega, Lori Wachter, David White
Radicalized Terrorism has become one of the most critical threats in the world. The Intelligence Community has a critical lack of radicalization tradecraft. The LAS collaborated with mission partners to understand their greatest needs. The LAS spent a year developing tradecraft that characterizes the path to radicalization by identify contributing risk factors, prioritizes persons of interest, and measures messaging impact on the audience. Using techniques such as hackathons, the group developed methods to help gather and analyze open-source data, including the dark web and add methodology and scientific rigor to analysis. This foundational research has led to designs for an analyst tool to be prototyped to the IC in 2018.
Community Detection with Overlapping Stochastic Block Models: Fundamental and Algorithmic Thresholds
Vaishakhi Mayya, Galen Reeves
The problem of community detection is to identify important clusters in a network. Over the past several years, there has been a huge surge in activity on this problem from several fields. Within the statistics literature, researchers have studied increasingly sophisticated models that allow for overlapping and hierarchal structures. At the same time, a separate but closely related line of research within statistical physics, computer science, and information theory has developed increasingly sophisticated methods inspired by message passing algorithms. The contribution of this work is to bring these two different approaches together in the context of a stochastic block model (SBM) with overlapping communities. We show how the Kesten-Stigum bound can provide insight into how the ability to detect communities depends on the degree of overlap. We also present numerical experiments demonstrating the tradeoffs between methods based on spectral clustering and methods based on belief propagation.
Design Thinking Through Design Research
Sharon Joines, Andres Tellez, Byungsoo Kim, Hongyang Liu, Jennifer Peavey, Catalina Salamanca
The Design Thinking through Design Research short course focused on introducing participants to design thinking through a week-long immersive design experience. Participants engaged in a situated design challenge for which they applied a variety of design methods to develop solutions to help investigators of different backgrounds and levels of expertise to conduct open source research. Participants explored primary and secondary sources; analyzed and synthesized information; proposed and evaluated design solutions; and materialized and communicated design alternatives using a variety of tools for presentation and representation. Based on the insights and lessons learned, it is recommended that furture versions: revise the prototyping tools provided to participants so that they support well both creativity and collaboration; propose research activities that provide enough structure that allows for a rigorous data collection process and, at the same time, provide enough autonomy so that participants can build their research planning skills; set in place a rule-based system for forming groups that promote diversity (members from different parent organizations, backgrounds, roles, and gender) and avoid conflicts between opposite personalities.
A Longitudinal Study of LAS Participation
Sharon Joines, Andres Tellez
The proposed longitudinal study aims to assess the impact of the LAS experience on the careers of LAS-G members and the work they do once they are back at their parent organizations. The indicators of impact were identified through a dozen of ethnographic interviews, a participatory data collection session with LAS researchers and analysts, and a continuous conversation with the Lab leadership. These indicators are as follows: (1) Soft Skills Development, (2) Connections and Referrals, (3) Work Products and Publications, and (4) Transitions and Applications. The proposed methodology features a panel study that follows a mixed-methods research strategy that, if implemented, will be used to collect and analyze diverse data from consecutive cohorts of government LAS performers while they are at the Lab and for up to 8 years after they have transitioned to a different organization. The methods proposed to collect and analyze these data are pre- and post-tests to measure soft skills, surveys to track connections and referrals, analysis of participants’ CVs, and semi-structured interviews.
Collaborative Report Generation
Hongyang Liu, Byungsoo Kim, Ruth Tayloe, Sharon Joines
Many decisions are made based on information gathered by an individual/group (or automatically generated), analyzed by a second different individual/group, and interpreted/decisions made by yet another. Understanding the communication pipeline, more specifically the reports generated to convey information, highlight opportunities for improvement within the pipeline which may affect transparency (veracity), timeliness (efficiency), quality, and accuracy (validity and collaborative perspective taking). Therefore, the purpose of the study was to document current report generation (consumption) strategies and associated collaboration methods used by analysts in multiple communities, including intelligence, law enforcement, legal, financial, and power system control (SCADA). Current report generation (consumption) strategies and associated collaboration methods were collected by interviewing seven stakeholders and by collecting eight responses via an online survey for analysts from IC who cannot be interviewed. The results of this study will help the intelligence analysts’ community to understand the process of report generation (consumption) and associated opportunities for and barriers to collaborative report generation as well as lead to improved report generation efficiency, quality, and veracity. Meanwhile, the outcome of this investigation will benefit stakeholders in developing improved processes for audience-specific reporting and their collaborative report generation.
Diversity and Performance: Bias and the Not-so Hidden Reasons for Disparity
Carmen A. Vazquez, Mariza Marrero
Diversity is about race, ethnicity, gender, age, etc. It is also about bringing different perspectives to problem solving, imagining different outcomes, and leveraging differences for the betterment of our mission and the Intelligence Community. Diversity is about equality, about opportunity, about moving removing stereotypes, and leveling the playing field. In the Intelligence Community, we would like to think that we have come a long way when it comes to diversity and equality, but our progress is much more modest. Through two separate but linked research efforts, we examined the effect of bias on individual and teams and how bias influences leaders and the unintended consequences on performance and career advancement of their direct reports. Through this examination we offer possible explanations for why the Intelligence Community continues to fall behind and offer potential micro-solutions through novel approaches.
Defence and Security at the UK’s National Data Science Institute
Mark Briers, Ben Tagger, Paul Jones
This poster presents an overview of the Alan Turing Institute and its ongoing and planned activities under the Defence and Security Programme for 2017-18. We will highlight research areas where we already have a joint interest with LAS, and we hope to stimulate discussion on potential future collaboration opportunities.
Supporting a Cross-Sector, Interdisciplinary Organization
Eli Typhina, Jessica Katz Jameson
Our research sought to identify the collective identity that guides LAS members in collaborative work and the materials and events that could further support cross-sector, interdisciplinary work at LAS.
Immersive Collaboration among the Intelligence Community, Academy, and Industry: Communication that Cultivates Discovery and Translation
Jessica Jameson, Sharon Joines, Beverly Tyler, Kathleen Vogel
The poster presents an overview of a book in progress co-authored by Jameson, Joines, Tyler, and Vogel, with contributions from additional members of the LAS. The purpose of the book is to document, analyze, and critique the first five years of a laboratory designed to support big data analytics through immersive collaboration of government analysts, academics, and industry partners. While other books have explained critical aspects of collaboration, this book will illustrate unique and innovative features of LAS that have led to new discoveries and translation of research projects to the intelligence community. This book responds to the mandate from the Office of the Director of National Intelligence to leverage outside expertise as part of the analytic process and answers the broader call to enhance our theoretical and practical knowledge of interinstitutional and interdisciplinary collaboration.
Devin Shackle, Matt Schmidt, Lori Wachter
Traditional research publications are not always the best conduit for transferring the knowledge from the LAS back to mission. This year, the Affiliations Exemplar launched some additional activities to investigate collaborative ways to transfer our unclassified research and tradecraft to mission space. While these activities varied from two-hour long sprints, to day-long hack-a-thons, to week-long workshops, all were generally designed to be group-based activities that provided opportunities for hands-on learning focused around a single topic, technique, or tool.
Feedback from participants in these activities revealed a variety of positive outcomes. Analysts gained a finer-grained understanding of a topic or the application of a technique or tool; developers gained valuable feedback on how their tools were or could provide value to potential users; and researchers came away with potential new areas of investigation. These activities appear to provide a valuable complement to traditional methods of knowledge transfer.
Jody Coward, Dawn Hendricks, Matt Schmidt
The objective of the various research transition efforts at LAS is to help build on the innovation at LAS to provide novel benefits to mission problems. Toward this goal, the research transition efforts help link LAS research with mission problems; inform current and potential partners, collaborators, and stakeholders about the progress of various LAS efforts; and create a shared set of expectations for what research transitions looks like. These research transition efforts are shared between LAS performers, NCSU’s I2I team and government personnel responsible for the operations of applications in the mission space.